From b39a22ef2b4b1ad858ce889ed174b8cec88270a7 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Fri, 6 Jun 2025 12:07:09 -0700 Subject: [PATCH 01/28] checkpoint: initial weighted --- benchmark_example.py | 277 ++++++++++++++++++++ config/benchmark_config.json | 51 ++++ docs/benchmarking_system_prd.md | 212 +++++++++++++++ src/benchmark/README.md | 440 +++++++++++++++++++++++++++++++- src/benchmark/__init__.py | 23 ++ src/benchmark/config.py | 107 ++++++++ src/benchmark/data_loader.py | 193 ++++++++++++++ src/benchmark/evaluator.py | 218 ++++++++++++++++ src/benchmark/metrics.py | 322 +++++++++++++++++++++++ src/benchmark/models.py | 313 +++++++++++++++++++++++ src/benchmark/pipeline.py | 406 +++++++++++++++++++++++++++++ src/benchmark/reporting.py | 395 ++++++++++++++++++++++++++++ 12 files changed, 2954 insertions(+), 3 deletions(-) create mode 100644 benchmark_example.py create mode 100644 config/benchmark_config.json create mode 100644 docs/benchmarking_system_prd.md create mode 100644 src/benchmark/config.py create mode 100644 src/benchmark/data_loader.py create mode 100644 src/benchmark/evaluator.py create mode 100644 src/benchmark/metrics.py create mode 100644 src/benchmark/models.py create mode 100644 src/benchmark/pipeline.py create mode 100644 src/benchmark/reporting.py diff --git a/benchmark_example.py b/benchmark_example.py new file mode 100644 index 0000000..402fdfe --- /dev/null +++ b/benchmark_example.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python3 +""" +Example usage of the AutoGKB benchmarking system. + +This script demonstrates how to use the benchmarking framework to evaluate +language models on pharmacogenomic knowledge extraction tasks. + +The benchmark system now supports two modes: + +1. **Separated Response Generation and Evaluation**: + - Generate model responses and save to JSONL files + - Evaluate JSONL files separately to get scores + - Allows for response caching and reuse across different evaluation metrics + +2. **Combined Mode** (legacy): + - Generate responses and evaluate in one step + +Usage examples: + python benchmark_example.py # Run full benchmark + python benchmark_example.py --validate # Quick validation + python benchmark_example.py --evaluate file.jsonl # Evaluate specific response file +""" + +import os +import logging +from pathlib import Path + +from src.benchmark import BenchmarkPipeline, BenchmarkConfig + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) + +logger = logging.getLogger(__name__) + + +def main(): + """Main example demonstrating benchmark usage.""" + + # 1. Create configuration + config = BenchmarkConfig( + data_dir=Path("data"), + articles_dir=Path("data/articles"), + benchmark_dir=Path("data/benchmark"), + output_dir=Path("benchmark_results"), + model_name="mock", # Start with mock model for testing + max_articles=10, # Limit for example + batch_size=5 + ) + + # 2. Initialize pipeline + pipeline = BenchmarkPipeline(config) + + # 3. Validate setup + logger.info("Validating benchmark setup...") + validation = pipeline.validate_setup() + + if not validation["config_valid"]: + logger.error("Setup validation failed:") + for issue in validation["issues"]: + logger.error(f" - {issue}") + return + + logger.info("Setup validation passed!") + logger.info(f"Data statistics: {validation['data_statistics']}") + + # 4. Get data statistics + logger.info("Loading data statistics...") + stats = pipeline.get_data_statistics("train") + logger.info(f"Training data: {stats['total_samples']} samples") + logger.info(f"Average article length: {stats['avg_article_length']:.0f} characters") + + # 5. Run benchmark on test models + model_configs = [ + { + "name": "mock_model", + "model_name": "mock" + } + ] + + # Add real models if API keys are available + if os.getenv("OPENAI_API_KEY"): + model_configs.append({ + "name": "gpt-4", + "model_name": "gpt-4", + "api_key": os.getenv("OPENAI_API_KEY"), + "temperature": 0.0, + "max_tokens": 4000 + }) + + if os.getenv("ANTHROPIC_API_KEY"): + model_configs.append({ + "name": "claude-3-sonnet", + "model_name": "claude-3-sonnet-20240229", + "api_key": os.getenv("ANTHROPIC_API_KEY"), + "temperature": 0.0, + "max_tokens": 4000 + }) + + # 6. Generate responses first (separate from evaluation) + logger.info(f"Generating responses with {len(model_configs)} models...") + + try: + # Step 1: Generate responses and save to JSONL files + response_files = pipeline.generate_responses(model_configs, split="train") # Using train for example + + logger.info(f"Generated {len(response_files)} response files:") + for model_name, response_file in response_files.items(): + logger.info(f" {model_name}: {response_file}") + + # Step 2: Evaluate each response file separately + logger.info("Evaluating response files...") + results = {} + for model_name, response_file in response_files.items(): + logger.info(f"Evaluating {model_name} responses...") + result = pipeline.evaluate_responses_file(response_file) + results[model_name] = result + + # 7. Print summary results + logger.info("\n" + "="*50) + logger.info("BENCHMARK RESULTS SUMMARY") + logger.info("="*50) + + for model_name, result in results.items(): + metrics = result.aggregate_metrics + logger.info(f"\nModel: {model_name}") + logger.info(f" Total samples: {result.total_samples}") + logger.info(f" Successful predictions: {result.successful_predictions}") + logger.info(f" Success rate: {result.successful_predictions/result.total_samples*100:.1f}%") + logger.info(f" Mean overall score: {metrics.get('mean_overall_score', 0):.3f}") + logger.info(f" Mean weighted score: {metrics.get('mean_weighted_score', 0):.3f}") + + # Show top performing fields + field_stats = metrics.get('field_statistics', {}) + if field_stats: + best_fields = sorted( + field_stats.items(), + key=lambda x: x[1].get('mean_score', 0), + reverse=True + )[:3] + + logger.info(" Top performing fields:") + for field, stats in best_fields: + score = stats.get('mean_score', 0) + exact_match = stats.get('exact_match_rate', 0) * 100 + logger.info(f" {field}: {score:.3f} (exact match: {exact_match:.1f}%)") + + # 8. Analyze a specific sample + if results and config.max_articles and config.max_articles > 0: + logger.info("\n" + "="*50) + logger.info("SAMPLE ANALYSIS") + logger.info("="*50) + + # Get first PMCID from results + first_result = next(iter(results.values())) + if first_result.sample_scores: + sample_pmcid = first_result.sample_scores[0].pmcid + + logger.info(f"Analyzing sample: {sample_pmcid}") + + # Analyze with first available model + first_model_config = model_configs[0] + analysis = pipeline.analyze_sample( + sample_pmcid, + first_model_config, + split="train" + ) + + logger.info(f"Article title: {analysis['article_title']}") + logger.info(f"Model: {analysis['model']}") + + if analysis['scores']: + logger.info(f"Overall score: {analysis['scores']['overall_score']:.3f}") + logger.info(f"Weighted score: {analysis['scores']['weighted_score']:.3f}") + + logger.info("\n" + "="*50) + logger.info("Benchmark completed successfully!") + logger.info(f"Results saved to: {config.output_dir}") + logger.info("="*50) + + except Exception as e: + logger.error(f"Benchmark failed: {e}") + raise + + +def run_quick_validation(): + """Quick validation without running full benchmark.""" + config = BenchmarkConfig(max_articles=1) + pipeline = BenchmarkPipeline(config) + + validation = pipeline.validate_setup() + + print("=== BENCHMARK VALIDATION ===") + print(f"Config valid: {validation['config_valid']}") + print(f"Data available: {validation['data_available']}") + + if validation.get('data_statistics'): + stats = validation['data_statistics'] + print(f"Train samples: {stats.get('train_samples', 0)}") + print(f"Val samples: {stats.get('val_samples', 0)}") + print(f"Test samples: {stats.get('test_samples', 0)}") + + print("\nModel accessibility:") + for model, accessible in validation.get('models_accessible', {}).items(): + print(f" {model}: {'✓' if accessible else '✗'}") + + if validation.get('issues'): + print("\nIssues found:") + for issue in validation['issues']: + print(f" - {issue}") + + return validation['config_valid'] + + +def evaluate_response_file(response_file_path: str): + """Example of evaluating a standalone JSONL response file.""" + logger.info(f"Evaluating standalone response file: {response_file_path}") + + # Create minimal config for evaluation only + config = BenchmarkConfig( + data_dir=Path("data"), + articles_dir=Path("data/articles"), + benchmark_dir=Path("data/benchmark"), + output_dir=Path("benchmark_results") + ) + + # Initialize pipeline + pipeline = BenchmarkPipeline(config) + + # Evaluate the response file + try: + result = pipeline.evaluate_responses_file(Path(response_file_path)) + + logger.info("\n" + "="*50) + logger.info("EVALUATION RESULTS") + logger.info("="*50) + logger.info(f"Model: {result.model_name}") + logger.info(f"Total samples: {result.total_samples}") + logger.info(f"Successful predictions: {result.successful_predictions}") + logger.info(f"Success rate: {result.successful_predictions/result.total_samples*100:.1f}%") + + metrics = result.aggregate_metrics + logger.info(f"Mean overall score: {metrics.get('mean_overall_score', 0):.3f}") + logger.info(f"Mean weighted score: {metrics.get('mean_weighted_score', 0):.3f}") + + # Show field performance + field_stats = metrics.get('field_statistics', {}) + if field_stats: + logger.info("\nField performance:") + for field, stats in field_stats.items(): + score = stats.get('mean_score', 0) + exact_match = stats.get('exact_match_rate', 0) * 100 + logger.info(f" {field}: {score:.3f} (exact match: {exact_match:.1f}%)") + + return result + + except Exception as e: + logger.error(f"Evaluation failed: {e}") + raise + + +if __name__ == "__main__": + import sys + + if len(sys.argv) > 1 and sys.argv[1] == "--validate": + # Quick validation mode + success = run_quick_validation() + sys.exit(0 if success else 1) + elif len(sys.argv) > 2 and sys.argv[1] == "--evaluate": + # Evaluate specific response file + response_file = sys.argv[2] + evaluate_response_file(response_file) + else: + # Full benchmark mode + main() \ No newline at end of file diff --git a/config/benchmark_config.json b/config/benchmark_config.json new file mode 100644 index 0000000..0ced4f1 --- /dev/null +++ b/config/benchmark_config.json @@ -0,0 +1,51 @@ +{ + "data_dir": "data", + "articles_dir": "data/articles", + "benchmark_dir": "data/benchmark", + "output_dir": "benchmark_results", + + "model_name": "claude-3-sonnet-20240229", + "model_params": {}, + "max_tokens": 4000, + "temperature": 0.0, + + "batch_size": 10, + "max_articles": null, + "random_seed": 42, + + "exact_match_fields": [ + "gene", + "drugs", + "significance", + "direction_of_effect", + "phenotype_category", + "specialty_population" + ], + + "fuzzy_match_fields": [ + "variant_haplotypes", + "alleles", + "population_phenotypes_or_diseases" + ], + + "semantic_match_fields": [ + "sentence", + "notes" + ], + + "field_weights": { + "gene": 1.0, + "drugs": 1.0, + "variant_haplotypes": 0.8, + "significance": 1.2, + "direction_of_effect": 1.2, + "phenotype_category": 1.0, + "sentence": 0.8, + "alleles": 0.8, + "specialty_population": 0.6, + "population_phenotypes_or_diseases": 0.6 + }, + + "save_predictions": true, + "save_detailed_results": true +} \ No newline at end of file diff --git a/docs/benchmarking_system_prd.md b/docs/benchmarking_system_prd.md new file mode 100644 index 0000000..ed397f5 --- /dev/null +++ b/docs/benchmarking_system_prd.md @@ -0,0 +1,212 @@ +# Language Model Benchmarking System for Pharmacogenomic Knowledge Extraction + +## Product Requirements Document (PRD) + +### 1. Overview + +This document outlines the requirements for developing a benchmarking system to evaluate language model (LM) performance on extracting structured pharmacogenomic annotations from scientific articles. + +### 2. Problem Statement + +Currently, there is no standardized way to measure how effectively language models can extract complex pharmacogenomic relationships from scientific literature. The AutoGKB project has curated data that maps scientific articles to structured annotations, providing an opportunity to create a robust evaluation framework. + +### 3. Objectives + +- **Primary:** Create a comprehensive benchmarking system to evaluate LM extraction capabilities +- **Secondary:** Establish baseline performance metrics for current state-of-the-art models +- **Tertiary:** Enable iterative improvement of extraction models through standardized evaluation + +### 4. Dataset Analysis + +#### 4.1 Training Data Structure (train.jsonl) +- **Format:** JSONL with structured pharmacogenomic annotations +- **Key Fields:** + - Article metadata (pmcid, title, pmid) + - Variant information (gene, variant_haplotypes, alleles) + - Drug associations (drugs, multiple_drugs_and_or) + - Clinical outcomes (phenotype_category, significance, direction_of_effect) + - Population context (specialty_population, population_types, population_phenotypes_or_diseases) + - Structured sentence representation with comparison groups + +#### 4.2 Article Repository (data/articles/) +- **Format:** Markdown files with standardized structure +- **Content:** Full-text scientific articles from PMC +- **Metadata:** Embedded author, journal, DOI, PMID, PMCID information +- **Structure:** Title, abstract, and full article content + +#### 4.3 Column Mapping (column_mapping.json) +- Maps database field names to human-readable descriptions +- Provides schema understanding for evaluation metrics + +### 5. Functional Requirements + +#### 5.1 Core Evaluation Framework + +**FR-1: Text-to-Structured Extraction Evaluation** +- Input: Scientific article text (markdown format) +- Expected Output: Structured JSON matching training data schema +- Evaluation: Field-level accuracy, relationship extraction precision/recall + +**FR-2: Multi-level Evaluation Metrics** +- **Entity Level:** Gene names, drug names, variant identifiers +- **Relationship Level:** Drug-gene associations, clinical outcomes +- **Semantic Level:** Direction of effect, significance assessment +- **Population Level:** Specialty populations, disease contexts + +**FR-3: Partial Credit Scoring** +- Exact match scoring for categorical fields +- Semantic similarity scoring for free-text fields +- Hierarchical scoring for complex nested relationships + +#### 5.2 Benchmark Tasks + +**Task 1: Basic Entity Extraction** +- Extract genes, drugs, variants from article text +- Metrics: Precision, Recall, F1-score per entity type + +**Task 2: Relationship Classification** +- Identify pharmacogenomic associations (significant/not significant) +- Classify direction of effect (increased/decreased/null) +- Metrics: Classification accuracy, confusion matrices + +**Task 3: Structured Sentence Generation** +- Generate standardized sentences describing relationships +- Compare against gold standard sentences +- Metrics: BLEU, ROUGE, semantic similarity scores + +**Task 4: Population Context Extraction** +- Identify specialty populations (Pediatric, etc.) +- Extract disease/phenotype contexts +- Metrics: Multi-label classification metrics + +#### 5.3 Evaluation Infrastructure + +**FR-4: Automated Evaluation Pipeline** +- Load articles and corresponding annotations +- Run LM inference on article content +- Compare outputs against gold standard +- Generate comprehensive evaluation reports + +**FR-5: Model Comparison Framework** +- Support multiple LM architectures (GPT, Claude, Llama, etc.) +- Standardized prompting strategies +- Fair comparison methodologies + +**FR-6: Error Analysis Tools** +- Categorize error types (missing entities, incorrect relationships) +- Provide detailed failure case analysis +- Generate improvement recommendations + +### 6. Technical Requirements + +#### 6.1 Input Processing +- Parse markdown articles to extract relevant text sections +- Handle article metadata extraction +- Support batch processing of multiple articles + +#### 6.2 Output Validation +- Validate LM outputs against expected schema +- Handle malformed JSON responses +- Implement fallback parsing strategies + +#### 6.3 Evaluation Metrics Implementation +- Exact string matching for categorical fields +- Fuzzy matching for drug/gene names (accounting for synonyms) +- Semantic embedding-based similarity for free-text fields +- Statistical significance testing for performance comparisons + +#### 6.4 Reporting and Visualization +- Generate detailed evaluation reports (HTML/PDF) +- Create performance comparison dashboards +- Provide per-article and aggregate statistics + +### 7. Data Requirements + +#### 7.1 Evaluation Splits +- **Training Set:** For prompt development and model fine-tuning +- **Validation Set:** For hyperparameter tuning and model selection +- **Test Set:** For final evaluation and comparison + +#### 7.2 Reference Standards +- Gold standard annotations from domain experts +- Inter-annotator agreement metrics +- Confidence scores for ambiguous cases + +### 8. Performance Requirements + +#### 8.1 Accuracy Targets +- **Entity Extraction:** >90% F1-score for genes and drugs +- **Relationship Classification:** >85% accuracy for significance determination +- **Overall Structured Output:** >80% field-level accuracy + +#### 8.2 Processing Requirements +- Support evaluation of 100+ articles within reasonable time +- Parallel processing capabilities for multiple models +- Memory-efficient handling of large language models + +### 9. Success Criteria + +#### 9.1 Primary Success Metrics +- Comprehensive benchmark suite covering all annotation fields +- Baseline performance established for 3+ different LM families +- Reproducible evaluation methodology + +#### 9.2 Secondary Success Metrics +- Error analysis reveals actionable improvement areas +- Performance differences between models are statistically significant +- Community adoption for standardized pharmacogenomic LM evaluation + +### 10. Implementation Phases + +#### Phase 1: Core Framework +- Basic entity extraction evaluation +- Simple relationship classification metrics +- Initial evaluation pipeline + +#### Phase 2: Advanced Evaluation +- Complex relationship extraction +- Semantic similarity metrics +- Population context evaluation + +#### Phase 3: Comprehensive Analysis +- Error categorization and analysis +- Model comparison tools +- Performance optimization recommendations + +### 11. Dependencies + +#### 11.1 Data Dependencies +- Access to complete article repository +- Validated annotation gold standards +- Column mapping configuration + +#### 11.2 Technical Dependencies +- Language model APIs or local model hosting +- Evaluation metric libraries (scikit-learn, etc.) +- Text processing libraries +- Visualization frameworks + +### 12. Risks and Mitigations + +#### 12.1 Data Quality Risks +- **Risk:** Inconsistent annotation quality +- **Mitigation:** Implement annotation validation and quality checks + +#### 12.2 Model Variability Risks +- **Risk:** Non-deterministic model outputs +- **Mitigation:** Multiple evaluation runs with statistical analysis + +#### 12.3 Evaluation Bias Risks +- **Risk:** Overfitting evaluation to specific model strengths +- **Mitigation:** Diverse evaluation tasks and metrics + +### 13. Future Considerations + +- Integration with automated annotation pipelines +- Real-time evaluation capabilities +- Extension to other biomedical domains +- Community benchmark competitions + +--- + +This PRD provides the foundation for developing a comprehensive language model benchmarking system that will enable systematic evaluation and improvement of pharmacogenomic knowledge extraction capabilities. \ No newline at end of file diff --git a/src/benchmark/README.md b/src/benchmark/README.md index d12e18e..3f4130f 100644 --- a/src/benchmark/README.md +++ b/src/benchmark/README.md @@ -1,4 +1,438 @@ -# Benchmark +# AutoGKB Benchmarking System -## Functions -1. Calculate the niave difference between an extracted variant and the ground truth variant on Variant Annotation ID +A comprehensive framework for evaluating language model performance on pharmacogenomic knowledge extraction from scientific articles. + +## Overview + +This benchmarking system provides standardized evaluation of language models' ability to extract structured pharmacogenomic annotations from scientific literature. It implements the specifications outlined in the [Benchmarking System PRD](../../docs/benchmarking_system_prd.md). + +### Key Features + +- **Separated Response Generation and Evaluation**: Generate model responses to JSONL files, then evaluate separately +- **Response Caching**: Reuse generated responses for different evaluation metrics +- **Flexible Evaluation**: Evaluate any JSONL response file from external models +- **Multi-model Support**: OpenAI, Anthropic, and custom models +- **Comprehensive Metrics**: Field-specific scoring with weighted aggregation + +## Quick Start + +### 1. Separated Response Generation and Evaluation (Recommended) + +```python +from pathlib import Path +from src.benchmark import BenchmarkPipeline, BenchmarkConfig + +# Create configuration +config = BenchmarkConfig( + data_dir=Path("data"), + max_articles=50, + output_dir=Path("benchmark_results") +) + +# Initialize pipeline +pipeline = BenchmarkPipeline(config) + +# Step 1: Generate responses to JSONL files +model_configs = [ + {"name": "claude-3-sonnet", "model_name": "claude-3-sonnet-20240229"}, + {"name": "gpt-4", "model_name": "gpt-4"} +] + +response_files = pipeline.generate_responses(model_configs, split="test") +# Output: {'claude-3-sonnet': Path('benchmark_results/responses/claude-3-sonnet_test_123456.jsonl'), ...} + +# Step 2: Evaluate response files separately +results = {} +for model_name, response_file in response_files.items(): + result = pipeline.evaluate_responses_file(response_file) + results[model_name] = result +``` + +### 2. Combined Mode (Legacy) + +```python +# Traditional approach - generates and evaluates in one step +results = pipeline.run_benchmark(model_configs, split="test") +``` + +### 3. Evaluate External Response Files + +```python +# Evaluate any JSONL response file (from external models/systems) +result = pipeline.evaluate_responses_file(Path("path/to/external_responses.jsonl")) +print(f"Score: {result.aggregate_metrics['mean_overall_score']:.3f}") +``` + +### 4. Using the Example Script + +```bash +# Validate setup +python benchmark_example.py --validate + +# Run full benchmark (separated workflow) +python benchmark_example.py + +# Evaluate specific response file +python benchmark_example.py --evaluate benchmark_results/responses/gpt-4_test_123456.jsonl +``` + +### 5. Configuration File + +```python +from src.benchmark import BenchmarkConfig + +# Load from JSON +config = BenchmarkConfig.from_file(Path("config/benchmark_config.json")) + +# Or create programmatically +config = BenchmarkConfig( + data_dir=Path("data"), + output_dir=Path("benchmark_results"), + max_articles=100 +) +``` + +## Architecture + +### Core Components + +1. **BenchmarkDataLoader**: Loads and preprocesses articles and annotations +2. **EvaluationMetrics**: Implements scoring functions for different field types +3. **LanguageModelInterface**: Abstracts different LM APIs (OpenAI, Anthropic, etc.) +4. **BenchmarkEvaluator**: Orchestrates prediction generation and evaluation +5. **BenchmarkPipeline**: Main interface for running benchmarks +6. **BenchmarkReporter**: Generates reports and visualizations + +### Data Flow + +#### Separated Workflow (Recommended) +``` +Articles (Markdown) + Annotations (JSONL) + ↓ +BenchmarkDataLoader → BenchmarkSample objects + ↓ +LanguageModel → Response Generation → JSONL Files + ↓ +JSONL Files → Evaluation → Scores + ↓ +BenchmarkReporter → Reports +``` + +#### Combined Workflow (Legacy) +``` +Articles (Markdown) + Annotations (JSONL) + ↓ +BenchmarkDataLoader → BenchmarkSample objects + ↓ +LanguageModel → Predictions → EvaluationMetrics → Scores + ↓ +BenchmarkReporter → Reports +``` + +## Evaluation Tasks + +### Task 1: Entity Extraction +- **Goal**: Extract genes, drugs, variants from articles +- **Metrics**: Precision, Recall, F1-score per entity type +- **Fields**: `gene`, `drugs`, `variant_haplotypes`, `alleles` + +### Task 2: Relationship Classification +- **Goal**: Classify pharmacogenomic associations and effects +- **Metrics**: Classification accuracy, confusion matrices +- **Fields**: `significance`, `direction_of_effect`, `phenotype_category` + +### Task 3: Structured Sentence Generation +- **Goal**: Generate standardized sentences describing relationships +- **Metrics**: BLEU, ROUGE, semantic similarity +- **Fields**: `sentence` + +### Task 4: Population Context Extraction +- **Goal**: Identify specialty populations and disease contexts +- **Metrics**: Multi-label classification metrics +- **Fields**: `specialty_population`, `population_phenotypes_or_diseases` + +## Scoring System + +### Field Types and Scoring + +1. **Exact Match Fields** (Score: 1.0 or 0.0) + - Categorical fields requiring precise matches + - Examples: `significance`, `direction_of_effect` + +2. **Fuzzy Match Fields** (Score: 0.0-1.0) + - Entity names allowing for variations + - Uses sequence similarity with thresholds + - Examples: `gene`, `drugs`, `variant_haplotypes` + +3. **Semantic Match Fields** (Score: 0.0-1.0) + - Free text requiring semantic understanding + - Uses token-based similarity (extensible to embeddings) + - Examples: `sentence`, `notes` + +### Weighted Scoring + +Fields are weighted by importance: +- **High importance** (1.2): `significance`, `direction_of_effect` +- **Standard importance** (1.0): `gene`, `drugs`, `phenotype_category` +- **Lower importance** (0.6-0.8): Population context fields + +## Model Support + +### Supported Models + +1. **OpenAI Models**: GPT-4, GPT-3.5-turbo +2. **Anthropic Models**: Claude-3-Sonnet, Claude-3-Haiku +3. **Mock Model**: For testing and development + +### Adding New Models + +```python +from src.benchmark.models import LanguageModelInterface, ModelResponse + +class MyCustomModel(LanguageModelInterface): + def generate(self, prompt: str, **kwargs) -> ModelResponse: + # Implementation here + pass + + def is_available(self) -> bool: + # Check availability + pass +``` + +## Output and Reporting + +### Generated Files + +#### Response Generation Phase +1. **Response Files** (`responses/{model}_{split}_{timestamp}.jsonl`): Model predictions in JSONL format + ```json + {"pmcid": "PMC123", "model_name": "gpt-4", "timestamp": 1234567890, "gene": "CYP2C9", "drugs": "warfarin", ...} + ``` + +#### Evaluation Phase +2. **Summary Report** (`summary.html`): Comparative overview +3. **Detailed Reports** (`{model}_detailed.json`): Per-model analysis +4. **Performance Comparison** (`performance_comparison.json`): Side-by-side metrics +5. **Intermediate Results** (`intermediate/{model}_{split}_{timestamp}.json`): Evaluation results + +### JSONL Response File Format + +Each line contains a complete model prediction: +```json +{ + "pmcid": "PMC1234567", + "model_name": "gpt-4", + "timestamp": 1703123456.789, + "gene": "CYP2C9", + "drugs": "warfarin", + "significance": "yes", + "direction_of_effect": "decreased", + "phenotype_category": "Dosage", + "sentence": "CYP2C9 variants are associated with decreased warfarin dose requirements.", + "alleles": "*2/*3", + "comparison_alleles_or_genotypes": "*1/*1" +} +``` + +### Export Formats + +- **JSONL**: Sample-level predictions and scores (primary format) +- **CSV**: Tabular format for statistical analysis +- **JSON**: Structured data for programmatic access + +## Configuration Options + +### Key Parameters + +```python +@dataclass +class BenchmarkConfig: + # Data paths + data_dir: Path + articles_dir: Path + benchmark_dir: Path + + # Model settings + model_name: str + temperature: float = 0.0 + max_tokens: int = 4000 + + # Evaluation settings + batch_size: int = 10 + max_articles: Optional[int] = None + + # Field weights and matching strategies + field_weights: Dict[str, float] + exact_match_fields: List[str] + fuzzy_match_fields: List[str] + semantic_match_fields: List[str] +``` + +## Advanced Usage + +### Single Sample Analysis + +```python +# Analyze specific sample in detail +analysis = pipeline.analyze_sample( + pmcid="PMC1234567", + model_config={"name": "gpt-4", "model_name": "gpt-4"}, + split="test" +) + +print(f"Prediction: {analysis['prediction']}") +print(f"Ground truth: {analysis['ground_truth']}") +print(f"Scores: {analysis['scores']}") +``` + +### Custom Evaluation Metrics + +```python +from src.benchmark.metrics import EvaluationMetrics + +# Create custom metrics with different weights +custom_metrics = EvaluationMetrics(field_weights={ + "gene": 2.0, # Higher weight for gene extraction + "significance": 1.5, + "drugs": 1.0 +}) + +evaluator = BenchmarkEvaluator(model, custom_metrics, schema, categorical_fields) +``` + +### Batch Processing + +```python +# Process large datasets efficiently +config.batch_size = 50 # Larger batches +config.max_articles = 1000 # Limit for faster iteration + +results = pipeline.run_benchmark(model_configs, split="test") +``` + +## API Reference + +### Core Classes + +- **BenchmarkPipeline**: Main interface + - `generate_responses()`: Generate model responses to JSONL files + - `evaluate_responses_file()`: Evaluate JSONL response file and return scores + - `run_benchmark()`: Complete benchmark (generates + evaluates) + - `validate_setup()`: Check configuration + - `analyze_sample()`: Single sample analysis + +- **BenchmarkConfig**: Configuration management + - `from_file()`: Load from JSON + - `validate()`: Check settings + +- **EvaluationMetrics**: Scoring functions + - `evaluate_sample()`: Score single prediction + - `aggregate_results()`: Compute summary statistics + +### Model Interfaces + +- **LanguageModelInterface**: Abstract base class +- **OpenAIModel**: OpenAI API integration +- **AnthropicModel**: Anthropic API integration +- **MockModel**: Testing model + +## Best Practices + +### 1. Development Workflow +1. Start with `MockModel` for testing +2. Use small `max_articles` for development +3. Validate setup before full runs +4. Monitor API costs and rate limits + +### 2. Production Evaluation +1. Use full test set for final evaluation +2. Set `temperature=0.0` for reproducibility +3. Run multiple evaluation rounds for statistical significance +4. Save intermediate results for recovery + +### 3. Performance Optimization +1. Use separated workflow to cache responses +2. Use appropriate batch sizes +3. Parallelize evaluation when feasible +4. Monitor memory usage with large datasets + +## Separated vs Combined Workflows + +### Benefits of Separated Workflow + +1. **Cost Efficiency**: Avoid re-running expensive API calls during metric development +2. **Flexibility**: Evaluate external model responses from any source +3. **Debugging**: Inspect intermediate responses for issues +4. **Parallelization**: Generate responses and evaluation can be distributed +5. **Metric Development**: Iterate on evaluation metrics without regenerating responses +6. **Comparison**: Evaluate same responses with different metric configurations + +### When to Use Each Approach + +**Use Separated Workflow When:** +- Developing or iterating on evaluation metrics +- Working with expensive API models +- Need to evaluate external model outputs +- Want to cache responses for multiple evaluations +- Distributing workload across systems + +**Use Combined Workflow When:** +- Quick one-off evaluations +- Working with local/fast models +- Simple benchmarking scenarios + +## Troubleshooting + +### Common Issues + +1. **Data Loading Errors** + - Check file paths in configuration + - Verify JSONL format integrity + - Ensure article files exist + +2. **Model API Errors** + - Verify API keys are set + - Check rate limits and quotas + - Handle network timeouts + +3. **Evaluation Errors** + - Check JSON parsing in model responses + - Validate field schema consistency + - Handle missing or malformed predictions + +### Debug Mode + +```python +import logging +logging.basicConfig(level=logging.DEBUG) + +# Enable detailed logging +pipeline = BenchmarkPipeline(config) +``` + +## Contributing + +### Adding Features + +1. **New Models**: Implement `LanguageModelInterface` +2. **New Metrics**: Extend `EvaluationMetrics` +3. **New Reports**: Add to `BenchmarkReporter` + +### Testing + +```bash +# Run validation +python benchmark_example.py --validate + +# Test with mock model +python -c " +from src.benchmark import * +config = BenchmarkConfig(model_name='mock', max_articles=1) +pipeline = BenchmarkPipeline(config) +results = pipeline.run_benchmark([{'name': 'mock', 'model_name': 'mock'}]) +" +``` + +## License + +See [LICENSE](../../LICENSES/) for details. \ No newline at end of file diff --git a/src/benchmark/__init__.py b/src/benchmark/__init__.py index e69de29..cb9c622 100644 --- a/src/benchmark/__init__.py +++ b/src/benchmark/__init__.py @@ -0,0 +1,23 @@ +""" +AutoGKB Benchmarking System + +A comprehensive framework for evaluating language model performance on +pharmacogenomic knowledge extraction from scientific articles. +""" + +from .data_loader import BenchmarkDataLoader +from .evaluator import BenchmarkEvaluator +from .metrics import EvaluationMetrics +from .models import LanguageModelInterface +from .pipeline import BenchmarkPipeline +from .config import BenchmarkConfig + +__version__ = "1.0.0" +__all__ = [ + "BenchmarkDataLoader", + "BenchmarkEvaluator", + "EvaluationMetrics", + "LanguageModelInterface", + "BenchmarkPipeline", + "BenchmarkConfig" +] \ No newline at end of file diff --git a/src/benchmark/config.py b/src/benchmark/config.py new file mode 100644 index 0000000..5283e2a --- /dev/null +++ b/src/benchmark/config.py @@ -0,0 +1,107 @@ +""" +Configuration management for the benchmarking system. +""" + +from dataclasses import dataclass, field +from pathlib import Path +from typing import Dict, List, Optional, Any +import json + + +@dataclass +class BenchmarkConfig: + """Configuration class for benchmark system.""" + + # Data paths + data_dir: Path = field(default_factory=lambda: Path("data")) + articles_dir: Path = field(default_factory=lambda: Path("data/articles")) + benchmark_dir: Path = field(default_factory=lambda: Path("data/benchmark")) + + # Model configuration + model_name: str = "claude-3-sonnet" + model_params: Dict[str, Any] = field(default_factory=dict) + max_tokens: int = 4000 + temperature: float = 0.0 + + # Evaluation settings + batch_size: int = 10 + max_articles: Optional[int] = None + random_seed: int = 42 + + # Metrics configuration + exact_match_fields: List[str] = field(default_factory=lambda: [ + "gene", "drugs", "significance", "direction_of_effect", + "phenotype_category", "specialty_population" + ]) + + fuzzy_match_fields: List[str] = field(default_factory=lambda: [ + "variant_haplotypes", "alleles", "population_phenotypes_or_diseases" + ]) + + semantic_match_fields: List[str] = field(default_factory=lambda: [ + "sentence", "notes" + ]) + + # Scoring weights + field_weights: Dict[str, float] = field(default_factory=lambda: { + "gene": 1.0, + "drugs": 1.0, + "variant_haplotypes": 0.8, + "significance": 1.2, + "direction_of_effect": 1.2, + "phenotype_category": 1.0, + "sentence": 0.1, + "alleles": 0.8, + "specialty_population": 0.6, + "population_phenotypes_or_diseases": 0.6 + }) + + # Output configuration + output_dir: Path = field(default_factory=lambda: Path("results")) + save_predictions: bool = True + save_detailed_results: bool = True + + @classmethod + def from_file(cls, config_path: Path) -> "BenchmarkConfig": + """Load configuration from JSON file.""" + with open(config_path, 'r') as f: + config_dict = json.load(f) + + # Convert path strings to Path objects + for key in ['data_dir', 'articles_dir', 'benchmark_dir', 'output_dir']: + if key in config_dict: + config_dict[key] = Path(config_dict[key]) + + return cls(**config_dict) + + def to_file(self, config_path: Path) -> None: + """Save configuration to JSON file.""" + config_dict = {} + for key, value in self.__dict__.items(): + if isinstance(value, Path): + config_dict[key] = str(value) + else: + config_dict[key] = value + + with open(config_path, 'w') as f: + json.dump(config_dict, f, indent=2) + + def validate(self) -> None: + """Validate configuration parameters.""" + if not self.data_dir.exists(): + raise ValueError(f"Data directory does not exist: {self.data_dir}") + + if not self.articles_dir.exists(): + raise ValueError(f"Articles directory does not exist: {self.articles_dir}") + + if not self.benchmark_dir.exists(): + raise ValueError(f"Benchmark directory does not exist: {self.benchmark_dir}") + + if self.batch_size <= 0: + raise ValueError("Batch size must be positive") + + if self.temperature < 0 or self.temperature > 2: + raise ValueError("Temperature must be between 0 and 2") + + # Create output directory if it doesn't exist + self.output_dir.mkdir(parents=True, exist_ok=True) \ No newline at end of file diff --git a/src/benchmark/data_loader.py b/src/benchmark/data_loader.py new file mode 100644 index 0000000..4996aac --- /dev/null +++ b/src/benchmark/data_loader.py @@ -0,0 +1,193 @@ +""" +Data loading and preprocessing utilities for the benchmarking system. +""" + +import json +import re +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Any +from dataclasses import dataclass +import logging + +logger = logging.getLogger(__name__) + + +@dataclass +class BenchmarkSample: + """A single benchmark sample containing article and annotation.""" + pmcid: str + article_title: str + article_content: str + annotation: Dict[str, Any] + article_path: str + + +class BenchmarkDataLoader: + """Loads and preprocesses benchmark data.""" + + def __init__(self, data_dir: Path, articles_dir: Path, benchmark_dir: Path): + self.data_dir = data_dir + self.articles_dir = articles_dir + self.benchmark_dir = benchmark_dir + self.column_mapping = self._load_column_mapping() + + def _load_column_mapping(self) -> Dict[str, str]: + """Load column mapping configuration.""" + mapping_path = self.benchmark_dir / "column_mapping.json" + if mapping_path.exists(): + with open(mapping_path, 'r') as f: + return json.load(f) + return {} + + def load_split(self, split: str = "train") -> List[BenchmarkSample]: + """Load a specific data split (train/val/test).""" + split_path = self.benchmark_dir / f"{split}.jsonl" + if not split_path.exists(): + raise FileNotFoundError(f"Split file not found: {split_path}") + + samples = [] + with open(split_path, 'r') as f: + for line_num, line in enumerate(f, 1): + try: + annotation = json.loads(line.strip()) + sample = self._create_sample(annotation) + if sample: + samples.append(sample) + except json.JSONDecodeError as e: + logger.warning(f"Invalid JSON at line {line_num}: {e}") + except Exception as e: + logger.warning(f"Error processing line {line_num}: {e}") + + logger.info(f"Loaded {len(samples)} samples from {split} split") + return samples + + def _create_sample(self, annotation: Dict[str, Any]) -> Optional[BenchmarkSample]: + """Create a benchmark sample from annotation data.""" + pmcid = annotation.get("pmcid") + if not pmcid: + logger.warning("Missing PMCID in annotation") + return None + + # Load article content + article_content = self._load_article_content(pmcid) + if not article_content: + logger.warning(f"Could not load article content for {pmcid}") + return None + + return BenchmarkSample( + pmcid=pmcid, + article_title=annotation.get("article_title", ""), + article_content=article_content, + annotation=annotation, + article_path=annotation.get("article_path", f"articles/{pmcid}.md") + ) + + def _load_article_content(self, pmcid: str) -> Optional[str]: + """Load article content from markdown file.""" + article_path = self.articles_dir / f"{pmcid}.md" + if not article_path.exists(): + return None + + try: + with open(article_path, 'r', encoding='utf-8') as f: + content = f.read() + return self._preprocess_article_content(content) + except Exception as e: + logger.warning(f"Error reading article {pmcid}: {e}") + return None + + def _preprocess_article_content(self, content: str) -> str: + """Preprocess article content for model consumption.""" + # Remove excessive whitespace + content = re.sub(r'\n\s*\n\s*\n', '\n\n', content) + + # Clean up markdown formatting + content = re.sub(r'^#{1,6}\s+', '', content, flags=re.MULTILINE) # Remove header markers + content = re.sub(r'\*\*(.*?)\*\*', r'\1', content) # Remove bold formatting + content = re.sub(r'\*(.*?)\*', r'\1', content) # Remove italic formatting + content = re.sub(r'\[(.*?)\]\(.*?\)', r'\1', content) # Remove links, keep text + + # Remove metadata section if present + if content.startswith('# '): + # Find the end of metadata section (usually after first ## heading) + lines = content.split('\n') + start_idx = 0 + for i, line in enumerate(lines): + if line.startswith('## Abstract') or line.startswith('## Introduction'): + start_idx = i + break + content = '\n'.join(lines[start_idx:]) + + return content.strip() + + def get_field_schema(self) -> Dict[str, str]: + """Get the schema of annotation fields.""" + return { + "pmcid": "PubMed Central ID", + "article_title": "Title of the article", + "variant_annotation_id": "Unique identifier for the annotation", + "variant_haplotypes": "Genetic variant or haplotype information", + "gene": "Gene symbol or name", + "drugs": "Drug name(s) involved in the association", + "pmid": "PubMed ID", + "phenotype_category": "Category of phenotype (Dosage, Efficacy, Metabolism/PK, etc.)", + "significance": "Statistical significance (yes/no/not stated)", + "notes": "Additional notes or context", + "sentence": "Structured sentence describing the association", + "alleles": "Specific alleles involved", + "specialty_population": "Special population (Pediatric, etc.)", + "metabolizer_types": "Metabolizer phenotype", + "is_plural": "Grammatical form (Is/Are)", + "is_is_not_associated": "Association type (Associated with/Not associated with)", + "direction_of_effect": "Direction of effect (increased/decreased/null)", + "pd_pk_terms": "Pharmacodynamic/pharmacokinetic terms", + "multiple_drugs_and_or": "Conjunction for multiple drugs (and/or)", + "population_types": "Population description", + "population_phenotypes_or_diseases": "Disease or phenotype context", + "multiple_phenotypes_or_diseases_and_or": "Conjunction for multiple conditions", + "comparison_alleles_or_genotypes": "Comparison group genotypes", + "comparison_metabolizer_types": "Comparison metabolizer types" + } + + def get_categorical_fields(self) -> Dict[str, List[str]]: + """Get possible values for categorical fields.""" + return { + "significance": ["yes", "no", "not stated"], + "phenotype_category": [ + "Dosage", "Efficacy", "Metabolism/PK", "Other", + "Toxicity", "Dosage, Efficacy", "Dosage, Metabolism/PK", + "Efficacy, Metabolism/PK", "Other, Metabolism/PK" + ], + "direction_of_effect": ["increased", "decreased", None], + "is_plural": ["Is", "Are"], + "is_is_not_associated": ["Associated with", "Not associated with"], + "multiple_drugs_and_or": ["and", "or"], + "multiple_phenotypes_or_diseases_and_or": ["and", "or"], + "specialty_population": ["Pediatric", None] + } + + def get_statistics(self, samples: List[BenchmarkSample]) -> Dict[str, Any]: + """Get statistics about the loaded samples.""" + if not samples: + return {} + + stats = { + "total_samples": len(samples), + "unique_pmcids": len(set(s.pmcid for s in samples)), + "avg_article_length": sum(len(s.article_content) for s in samples) / len(samples), + } + + # Field completion rates + field_counts = {} + for sample in samples: + for field, value in sample.annotation.items(): + if field not in field_counts: + field_counts[field] = 0 + if value is not None and value != "": + field_counts[field] += 1 + + stats["field_completion_rates"] = { + field: count / len(samples) for field, count in field_counts.items() + } + + return stats \ No newline at end of file diff --git a/src/benchmark/evaluator.py b/src/benchmark/evaluator.py new file mode 100644 index 0000000..f2f9dbd --- /dev/null +++ b/src/benchmark/evaluator.py @@ -0,0 +1,218 @@ +""" +Core evaluation logic for the benchmarking system. +""" + +import json +import re +from typing import Dict, List, Any, Optional +from dataclasses import dataclass +import logging + +from .data_loader import BenchmarkSample +from .metrics import EvaluationMetrics, SampleScore +from .models import LanguageModelInterface, PharmacogenomicPromptBuilder + +logger = logging.getLogger(__name__) + + +@dataclass +class EvaluationResult: + """Result of evaluating a language model on the benchmark.""" + model_name: str + sample_scores: List[SampleScore] + aggregate_metrics: Dict[str, Any] + error_analysis: Dict[str, Any] + total_samples: int + successful_predictions: int + failed_predictions: List[str] # PMCIDs of failed predictions + + +class BenchmarkEvaluator: + """Main evaluator for the benchmarking system.""" + + def __init__( + self, + model: LanguageModelInterface, + metrics: EvaluationMetrics, + schema: Dict[str, str], + categorical_fields: Dict[str, List[str]] + ): + self.model = model + self.metrics = metrics + self.schema = schema + self.categorical_fields = categorical_fields + self.prompt_builder = PharmacogenomicPromptBuilder(schema, categorical_fields) + + def evaluate(self, samples: List[BenchmarkSample]) -> EvaluationResult: + """Evaluate the model on a list of benchmark samples.""" + logger.info(f"Starting evaluation of {self.model.model_name} on {len(samples)} samples") + + sample_scores = [] + failed_predictions = [] + + for i, sample in enumerate(samples): + logger.info(f"Processing sample {i+1}/{len(samples)}: {sample.pmcid}") + + try: + # Generate prediction + prediction = self._generate_prediction(sample) + + if prediction is None: + failed_predictions.append(sample.pmcid) + continue + + # Evaluate prediction + sample_score = self.metrics.evaluate_sample( + prediction, sample.annotation, sample.pmcid + ) + sample_scores.append(sample_score) + + except Exception as e: + logger.error(f"Error processing sample {sample.pmcid}: {e}") + failed_predictions.append(sample.pmcid) + + # Aggregate results + aggregate_metrics = self.metrics.aggregate_results(sample_scores) + error_analysis = self.metrics.generate_error_analysis(sample_scores) + + logger.info(f"Evaluation completed: {len(sample_scores)} successful, {len(failed_predictions)} failed") + + return EvaluationResult( + model_name=self.model.model_name, + sample_scores=sample_scores, + aggregate_metrics=aggregate_metrics, + error_analysis=error_analysis, + total_samples=len(samples), + successful_predictions=len(sample_scores), + failed_predictions=failed_predictions + ) + + def _generate_prediction(self, sample: BenchmarkSample) -> Optional[Dict[str, Any]]: + """Generate prediction for a single sample.""" + try: + # Build prompt + prompt = self.prompt_builder.build_extraction_prompt(sample.article_content) + + # Generate response + response = self.model.generate(prompt) + + # Parse JSON response + prediction = self._parse_model_response(response.content) + + if prediction is None: + logger.warning(f"Failed to parse response for {sample.pmcid}") + return None + + # Validate and clean prediction + prediction = self._validate_prediction(prediction, sample) + + return prediction + + except Exception as e: + logger.error(f"Error generating prediction for {sample.pmcid}: {e}") + return None + + def _parse_model_response(self, response_content: str) -> Optional[Dict[str, Any]]: + """Parse JSON from model response.""" + # Try to extract JSON from response + json_patterns = [ + r'```json\s*(\{.*?\})\s*```', # JSON in code blocks + r'```\s*(\{.*?\})\s*```', # JSON in generic code blocks + r'(\{.*?\})', # Direct JSON + ] + + for pattern in json_patterns: + matches = re.findall(pattern, response_content, re.DOTALL) + for match in matches: + try: + return json.loads(match) + except json.JSONDecodeError: + continue + + # If no JSON found, try parsing the entire response + try: + return json.loads(response_content.strip()) + except json.JSONDecodeError: + logger.warning("Could not parse JSON from model response") + return None + + def _validate_prediction( + self, + prediction: Dict[str, Any], + sample: BenchmarkSample + ) -> Dict[str, Any]: + """Validate and clean prediction.""" + validated = {} + + # Ensure required fields are present + for field_name in self.schema.keys(): + if field_name in ['variant_annotation_id', 'pmid', 'article_path']: + continue # Skip metadata fields + + value = prediction.get(field_name) + + # Clean and validate field value + cleaned_value = self._clean_field_value(field_name, value) + validated[field_name] = cleaned_value + + # Add metadata from sample + validated['pmcid'] = sample.pmcid + validated['article_title'] = sample.article_title + + return validated + + def _clean_field_value(self, field_name: str, value: Any) -> Any: + """Clean and validate a field value.""" + if value is None or value == "": + return None + + # Convert to string and clean + if isinstance(value, (int, float)): + value = str(value) + elif not isinstance(value, str): + value = str(value) + + value = value.strip() + + # Handle categorical fields + if field_name in self.categorical_fields: + allowed_values = self.categorical_fields[field_name] + + # Try exact match first + for allowed in allowed_values: + if allowed is None and value.lower() in ['null', 'none', '']: + return None + elif allowed is not None and value.lower() == str(allowed).lower(): + return allowed + + # Try partial match for some fields + if field_name in ['significance']: + if 'yes' in value.lower() or 'significant' in value.lower(): + return 'yes' + elif 'no' in value.lower() or 'not significant' in value.lower(): + return 'no' + else: + return 'not stated' + + # If no match found, keep original value but log warning + logger.warning(f"Invalid value '{value}' for categorical field '{field_name}'") + + return value + + def evaluate_single(self, sample: BenchmarkSample) -> Optional[SampleScore]: + """Evaluate a single sample (useful for debugging).""" + try: + prediction = self._generate_prediction(sample) + if prediction is None: + return None + + return self.metrics.evaluate_sample( + prediction, sample.annotation, sample.pmcid + ) + except Exception as e: + logger.error(f"Error evaluating single sample {sample.pmcid}: {e}") + return None + + def get_prediction_only(self, sample: BenchmarkSample) -> Optional[Dict[str, Any]]: + """Get prediction without evaluation (useful for analysis).""" + return self._generate_prediction(sample) \ No newline at end of file diff --git a/src/benchmark/metrics.py b/src/benchmark/metrics.py new file mode 100644 index 0000000..5d29008 --- /dev/null +++ b/src/benchmark/metrics.py @@ -0,0 +1,322 @@ +""" +Evaluation metrics and scoring functions for the benchmarking system. +""" + +import re +import json +from typing import Dict, List, Any, Tuple, Optional, Union +from dataclasses import dataclass +from difflib import SequenceMatcher +import logging + +logger = logging.getLogger(__name__) + + +@dataclass +class FieldScore: + """Score for a single field evaluation.""" + field_name: str + exact_match: bool + score: float + predicted: Any + expected: Any + error_type: Optional[str] = None + + +@dataclass +class SampleScore: + """Score for a complete sample evaluation.""" + pmcid: str + field_scores: List[FieldScore] + overall_score: float + weighted_score: float + + +class EvaluationMetrics: + """Comprehensive evaluation metrics for pharmacogenomic extraction.""" + + def __init__(self, field_weights: Optional[Dict[str, float]] = None): + self.field_weights = field_weights or {} + self.default_weight = 1.0 + + def evaluate_sample( + self, + predicted: Dict[str, Any], + expected: Dict[str, Any], + pmcid: str + ) -> SampleScore: + """Evaluate a single sample prediction against ground truth.""" + field_scores = [] + + for field_name in expected.keys(): + if field_name in ['variant_annotation_id', 'pmid', 'article_path']: + continue # Skip metadata fields + + field_score = self._evaluate_field( + field_name, + predicted.get(field_name), + expected.get(field_name) + ) + field_scores.append(field_score) + + # Calculate overall scores + if field_scores: + overall_score = sum(fs.score for fs in field_scores) / len(field_scores) + weighted_score = self._calculate_weighted_score(field_scores) + else: + overall_score = 0.0 + weighted_score = 0.0 + + return SampleScore( + pmcid=pmcid, + field_scores=field_scores, + overall_score=overall_score, + weighted_score=weighted_score + ) + + def _evaluate_field( + self, + field_name: str, + predicted: Any, + expected: Any + ) -> FieldScore: + """Evaluate a single field.""" + # Handle None values + if expected is None and predicted is None: + return FieldScore(field_name, True, 1.0, predicted, expected) + + if expected is None or predicted is None: + return FieldScore( + field_name, + False, + 0.0, + predicted, + expected, + error_type="missing_value" + ) + + # Convert to strings for comparison + pred_str = str(predicted).strip() if predicted is not None else "" + exp_str = str(expected).strip() if expected is not None else "" + + # Exact match check + if pred_str.lower() == exp_str.lower(): + return FieldScore(field_name, True, 1.0, predicted, expected) + + # Field-specific scoring + score = self._calculate_field_score(field_name, pred_str, exp_str) + + return FieldScore( + field_name, + False, + score, + predicted, + expected, + error_type=self._classify_error(pred_str, exp_str) + ) + + def _calculate_field_score(self, field_name: str, predicted: str, expected: str) -> float: + """Calculate field-specific score.""" + # Categorical fields - require exact match + categorical_fields = { + "significance", "direction_of_effect", "phenotype_category", + "is_plural", "is_is_not_associated", "multiple_drugs_and_or", + "specialty_population" + } + + if field_name in categorical_fields: + return 1.0 if predicted.lower() == expected.lower() else 0.0 + + # Entity fields - use fuzzy matching + entity_fields = { + "gene", "drugs", "variant_haplotypes", "alleles", + "comparison_alleles_or_genotypes" + } + + if field_name in entity_fields: + return self._fuzzy_match_score(predicted, expected) + + # Text fields - use semantic similarity + text_fields = { + "sentence", "notes", "population_phenotypes_or_diseases", + "pd_pk_terms", "population_types" + } + + if field_name in text_fields: + return self._semantic_similarity_score(predicted, expected) + + # Default: fuzzy matching + return self._fuzzy_match_score(predicted, expected) + + def _fuzzy_match_score(self, predicted: str, expected: str) -> float: + """Calculate fuzzy match score using sequence similarity.""" + if not predicted and not expected: + return 1.0 + if not predicted or not expected: + return 0.0 + + # Normalize strings + pred_norm = self._normalize_entity(predicted) + exp_norm = self._normalize_entity(expected) + + # Calculate similarity + similarity = SequenceMatcher(None, pred_norm, exp_norm).ratio() + + # Apply threshold - partial credit for close matches + if similarity >= 0.9: + return 1.0 + elif similarity >= 0.7: + return 0.8 + elif similarity >= 0.5: + return 0.5 + else: + return 0.0 + + def _semantic_similarity_score(self, predicted: str, expected: str) -> float: + """Calculate semantic similarity score for text fields.""" + if not predicted and not expected: + return 1.0 + if not predicted or not expected: + return 0.0 + + # Simple token-based similarity (can be enhanced with embeddings) + pred_tokens = set(self._tokenize_text(predicted.lower())) + exp_tokens = set(self._tokenize_text(expected.lower())) + + if not pred_tokens and not exp_tokens: + return 1.0 + if not pred_tokens or not exp_tokens: + return 0.0 + + # Jaccard similarity + intersection = len(pred_tokens & exp_tokens) + union = len(pred_tokens | exp_tokens) + + return intersection / union if union > 0 else 0.0 + + def _normalize_entity(self, entity: str) -> str: + """Normalize entity names for comparison.""" + # Remove common prefixes/suffixes + entity = re.sub(r'^(rs|CYP|COMT)', '', entity, flags=re.IGNORECASE) + + # Remove special characters and normalize spacing + entity = re.sub(r'[^\w\s\*\+\-]', ' ', entity) + entity = re.sub(r'\s+', ' ', entity).strip() + + return entity.lower() + + def _tokenize_text(self, text: str) -> List[str]: + """Tokenize text for semantic comparison.""" + # Simple word tokenization + tokens = re.findall(r'\b\w+\b', text.lower()) + # Remove common stop words + stop_words = {'is', 'are', 'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'} + return [token for token in tokens if token not in stop_words and len(token) > 2] + + def _classify_error(self, predicted: str, expected: str) -> str: + """Classify the type of error.""" + if not predicted: + return "missing_prediction" + if not expected: + return "unexpected_prediction" + + # Length-based classification + if len(predicted) < len(expected) * 0.5: + return "incomplete_extraction" + if len(predicted) > len(expected) * 2: + return "over_extraction" + + return "content_mismatch" + + def _calculate_weighted_score(self, field_scores: List[FieldScore]) -> float: + """Calculate weighted average score.""" + total_weight = 0.0 + weighted_sum = 0.0 + + for field_score in field_scores: + weight = self.field_weights.get(field_score.field_name, self.default_weight) + weighted_sum += field_score.score * weight + total_weight += weight + + return weighted_sum / total_weight if total_weight > 0 else 0.0 + + def aggregate_results(self, sample_scores: List[SampleScore]) -> Dict[str, Any]: + """Aggregate results across all samples.""" + if not sample_scores: + return {} + + # Overall statistics + overall_scores = [s.overall_score for s in sample_scores] + weighted_scores = [s.weighted_score for s in sample_scores] + + results = { + "total_samples": len(sample_scores), + "mean_overall_score": sum(overall_scores) / len(overall_scores), + "mean_weighted_score": sum(weighted_scores) / len(weighted_scores), + "min_score": min(overall_scores), + "max_score": max(overall_scores) + } + + # Field-level statistics + field_stats = {} + all_field_scores = {} + + for sample_score in sample_scores: + for field_score in sample_score.field_scores: + field_name = field_score.field_name + if field_name not in all_field_scores: + all_field_scores[field_name] = [] + all_field_scores[field_name].append(field_score) + + for field_name, scores in all_field_scores.items(): + field_values = [s.score for s in scores] + exact_matches = sum(1 for s in scores if s.exact_match) + + field_stats[field_name] = { + "mean_score": sum(field_values) / len(field_values), + "exact_match_rate": exact_matches / len(scores), + "total_predictions": len(scores), + "error_types": self._count_error_types(scores) + } + + results["field_statistics"] = field_stats + + # Performance by score ranges + score_ranges = { + "excellent": sum(1 for s in overall_scores if s >= 0.9), + "good": sum(1 for s in overall_scores if 0.7 <= s < 0.9), + "fair": sum(1 for s in overall_scores if 0.5 <= s < 0.7), + "poor": sum(1 for s in overall_scores if s < 0.5) + } + results["score_distribution"] = score_ranges + + return results + + def _count_error_types(self, field_scores: List[FieldScore]) -> Dict[str, int]: + """Count error types for a field.""" + error_counts = {} + for score in field_scores: + if score.error_type: + error_counts[score.error_type] = error_counts.get(score.error_type, 0) + 1 + return error_counts + + def generate_error_analysis(self, sample_scores: List[SampleScore]) -> Dict[str, Any]: + """Generate detailed error analysis.""" + error_analysis = { + "common_errors": {}, + "field_specific_issues": {}, + "difficult_samples": [] + } + + # Find samples with low scores + low_score_samples = [s for s in sample_scores if s.overall_score < 0.5] + error_analysis["difficult_samples"] = [ + { + "pmcid": s.pmcid, + "overall_score": s.overall_score, + "main_issues": [fs.field_name for fs in s.field_scores if fs.score < 0.3] + } + for s in low_score_samples[:10] # Top 10 most difficult + ] + + return error_analysis \ No newline at end of file diff --git a/src/benchmark/models.py b/src/benchmark/models.py new file mode 100644 index 0000000..9b06106 --- /dev/null +++ b/src/benchmark/models.py @@ -0,0 +1,313 @@ +""" +Language model interface and prompting system for the benchmarking framework. +""" + +import json +import time +from abc import ABC, abstractmethod +from typing import Dict, Any, List, Optional +from dataclasses import dataclass +import logging + +logger = logging.getLogger(__name__) + + +@dataclass +class ModelResponse: + """Response from a language model.""" + content: str + model_name: str + timestamp: float + metadata: Dict[str, Any] + raw_response: Optional[Any] = None + + +class LanguageModelInterface(ABC): + """Abstract interface for language models.""" + + def __init__(self, model_name: str, **kwargs): + self.model_name = model_name + self.config = kwargs + + @abstractmethod + def generate(self, prompt: str, **kwargs) -> ModelResponse: + """Generate response from the model.""" + pass + + @abstractmethod + def is_available(self) -> bool: + """Check if the model is available.""" + pass + + +class OpenAIModel(LanguageModelInterface): + """OpenAI GPT model interface.""" + + def __init__(self, model_name: str = "gpt-4", api_key: Optional[str] = None, **kwargs): + super().__init__(model_name, **kwargs) + self.api_key = api_key + try: + import openai + self.client = openai.OpenAI(api_key=api_key) + except ImportError: + raise ImportError("openai package required for OpenAI models") + + def generate(self, prompt: str, **kwargs) -> ModelResponse: + """Generate response using OpenAI API.""" + try: + response = self.client.chat.completions.create( + model=self.model_name, + messages=[{"role": "user", "content": prompt}], + temperature=kwargs.get("temperature", 0.0), + max_tokens=kwargs.get("max_tokens", 4000), + **{k: v for k, v in kwargs.items() if k not in ["temperature", "max_tokens"]} + ) + + return ModelResponse( + content=response.choices[0].message.content, + model_name=self.model_name, + timestamp=time.time(), + metadata={ + "usage": response.usage.dict() if response.usage else {}, + "finish_reason": response.choices[0].finish_reason + }, + raw_response=response + ) + except Exception as e: + logger.error(f"Error generating response from {self.model_name}: {e}") + raise + + def is_available(self) -> bool: + """Check if OpenAI API is available.""" + try: + self.client.models.list() + return True + except Exception: + return False + + +class AnthropicModel(LanguageModelInterface): + """Anthropic Claude model interface.""" + + def __init__(self, model_name: str = "claude-3-sonnet-20240229", api_key: Optional[str] = None, **kwargs): + super().__init__(model_name, **kwargs) + self.api_key = api_key + try: + import anthropic + self.client = anthropic.Anthropic(api_key=api_key) + except ImportError: + raise ImportError("anthropic package required for Anthropic models") + + def generate(self, prompt: str, **kwargs) -> ModelResponse: + """Generate response using Anthropic API.""" + try: + response = self.client.messages.create( + model=self.model_name, + messages=[{"role": "user", "content": prompt}], + temperature=kwargs.get("temperature", 0.0), + max_tokens=kwargs.get("max_tokens", 4000), + **{k: v for k, v in kwargs.items() if k not in ["temperature", "max_tokens"]} + ) + + return ModelResponse( + content=response.content[0].text, + model_name=self.model_name, + timestamp=time.time(), + metadata={ + "usage": { + "input_tokens": response.usage.input_tokens, + "output_tokens": response.usage.output_tokens + }, + "stop_reason": response.stop_reason + }, + raw_response=response + ) + except Exception as e: + logger.error(f"Error generating response from {self.model_name}: {e}") + raise + + def is_available(self) -> bool: + """Check if Anthropic API is available.""" + try: + # Simple test call + self.client.messages.create( + model=self.model_name, + messages=[{"role": "user", "content": "test"}], + max_tokens=1 + ) + return True + except Exception: + return False + + +class MockModel(LanguageModelInterface): + """Mock model for testing purposes.""" + + def __init__(self, model_name: str = "mock-model", **kwargs): + super().__init__(model_name, **kwargs) + + def generate(self, prompt: str, **kwargs) -> ModelResponse: + """Generate mock response.""" + # Simple mock response that tries to extract basic info + mock_content = ''' + { + "pmcid": "PMC1234567", + "article_title": "Mock Article", + "gene": "CYP2C9", + "drugs": "warfarin", + "significance": "yes", + "direction_of_effect": "decreased", + "phenotype_category": "Dosage", + "sentence": "Mock sentence describing the association.", + "alleles": "CT", + "comparison_alleles_or_genotypes": "CC" + } + ''' + + return ModelResponse( + content=mock_content.strip(), + model_name=self.model_name, + timestamp=time.time(), + metadata={"mock": True}, + raw_response=None + ) + + def is_available(self) -> bool: + """Mock model is always available.""" + return True + + +class PharmacogenomicPromptBuilder: + """Builds prompts for pharmacogenomic extraction tasks.""" + + def __init__(self, schema: Dict[str, str], categorical_fields: Dict[str, List[str]]): + self.schema = schema + self.categorical_fields = categorical_fields + + def build_extraction_prompt(self, article_content: str) -> str: + """Build prompt for extracting pharmacogenomic annotations.""" + schema_description = self._format_schema() + examples = self._get_examples() + + prompt = f""" +You are a pharmacogenomics expert tasked with extracting structured information from scientific articles. + +Your task is to analyze the following article and extract pharmacogenomic associations in the specified JSON format. + +## Schema Description: +{schema_description} + +## Important Guidelines: +1. Extract only information that is explicitly stated in the article +2. Use exact values from the categorical fields when applicable +3. For significance, use "yes" if statistically significant, "no" if explicitly not significant, "not stated" if unclear +4. For direction_of_effect, use "increased", "decreased", or null +5. Generate a structured sentence that captures the main finding +6. If multiple associations are described, focus on the primary/most significant one + +## Examples: +{examples} + +## Article to Analyze: +{article_content} + +## Your Response: +Please provide the extracted information as a valid JSON object: +""" + + return prompt + + def _format_schema(self) -> str: + """Format schema description for the prompt.""" + schema_lines = [] + for field, description in self.schema.items(): + if field in ['variant_annotation_id', 'pmid', 'article_path']: + continue # Skip metadata fields + + line = f"- {field}: {description}" + + # Add categorical options + if field in self.categorical_fields: + options = self.categorical_fields[field] + if None in options: + options = [str(opt) if opt is not None else "null" for opt in options] + line += f" (Options: {', '.join(str(opt) for opt in options)})" + + schema_lines.append(line) + + return '\n'.join(schema_lines) + + def _get_examples(self) -> str: + """Get example extractions for few-shot prompting.""" + examples = [ + { + "context": "Study found that patients with CYP2C9*3 variant required 25% lower warfarin doses compared to wild-type.", + "output": { + "gene": "CYP2C9", + "drugs": "warfarin", + "variant_haplotypes": "CYP2C9*3", + "significance": "yes", + "direction_of_effect": "decreased", + "phenotype_category": "Dosage", + "sentence": "CYP2C9*3 is associated with decreased dose of warfarin compared to wild-type.", + "alleles": "*3", + "comparison_alleles_or_genotypes": "wild-type" + } + }, + { + "context": "No significant association was found between rs123456 and response to drug X in this population.", + "output": { + "variant_haplotypes": "rs123456", + "drugs": "drug X", + "significance": "no", + "direction_of_effect": None, + "phenotype_category": "Efficacy", + "sentence": "rs123456 is not associated with response to drug X.", + "is_is_not_associated": "Not associated with" + } + } + ] + + example_text = "" + for i, example in enumerate(examples, 1): + example_text += f"\nExample {i}:\n" + example_text += f"Context: {example['context']}\n" + example_text += f"Output: {json.dumps(example['output'], indent=2)}\n" + + return example_text + + def build_validation_prompt(self, extraction: Dict[str, Any], article_content: str) -> str: + """Build prompt for validating extractions.""" + prompt = f""" +Please validate the following pharmacogenomic extraction against the source article. + +## Extraction to Validate: +{json.dumps(extraction, indent=2)} + +## Source Article: +{article_content[:2000]}... + +## Validation Task: +1. Check if the extracted information is accurate and supported by the article +2. Identify any missing key information +3. Flag any potential errors or inconsistencies + +Please provide a validation report in JSON format with: +- "is_valid": boolean +- "confidence_score": 0-1 +- "issues": list of identified problems +- "suggestions": list of improvements +""" + return prompt + + +def create_model(model_name: str, **kwargs) -> LanguageModelInterface: + """Factory function to create language model instances.""" + if model_name.startswith("gpt"): + return OpenAIModel(model_name, **kwargs) + elif model_name.startswith("claude"): + return AnthropicModel(model_name, **kwargs) + elif model_name == "mock": + return MockModel(model_name, **kwargs) + else: + raise ValueError(f"Unsupported model: {model_name}") \ No newline at end of file diff --git a/src/benchmark/pipeline.py b/src/benchmark/pipeline.py new file mode 100644 index 0000000..f8ec09a --- /dev/null +++ b/src/benchmark/pipeline.py @@ -0,0 +1,406 @@ +""" +Main benchmark pipeline orchestrating the entire evaluation process. +""" + +import json +import time +from pathlib import Path +from typing import Dict, List, Any, Optional +import logging + +from .config import BenchmarkConfig +from .data_loader import BenchmarkDataLoader, BenchmarkSample +from .metrics import EvaluationMetrics +from .models import create_model, LanguageModelInterface +from .evaluator import BenchmarkEvaluator, EvaluationResult +from .reporting import BenchmarkReporter + +logger = logging.getLogger(__name__) + + +class BenchmarkPipeline: + """Main pipeline for running benchmarks.""" + + def __init__(self, config: BenchmarkConfig): + self.config = config + config.validate() + + # Initialize components + self.data_loader = BenchmarkDataLoader( + config.data_dir, + config.articles_dir, + config.benchmark_dir + ) + + self.metrics = EvaluationMetrics(config.field_weights) + self.reporter = BenchmarkReporter(config.output_dir) + + # Get schema information + self.schema = self.data_loader.get_field_schema() + self.categorical_fields = self.data_loader.get_categorical_fields() + + def generate_responses( + self, + model_configs: List[Dict[str, Any]], + split: str = "test" + ) -> Dict[str, Path]: + """Generate model responses and save to JSONL files.""" + logger.info(f"Generating responses on {split} split with {len(model_configs)} models") + + # Load data + samples = self.data_loader.load_split(split) + if self.config.max_articles: + samples = samples[:self.config.max_articles] + + logger.info(f"Loaded {len(samples)} samples for response generation") + + # Generate responses for each model + response_files = {} + for model_config in model_configs: + model_name = model_config.get("name", "unknown") + logger.info(f"Generating responses for model: {model_name}") + + try: + # Create model + model = create_model(**model_config) + + # Check availability + if not model.is_available(): + logger.warning(f"Model {model_name} is not available, skipping") + continue + + # Generate responses file + response_file = self._generate_model_responses(model, samples, split) + response_files[model_name] = response_file + + except Exception as e: + logger.error(f"Error generating responses for model {model_name}: {e}") + continue + + logger.info(f"Response generation completed for {len(response_files)} models") + return response_files + + def evaluate_responses_file(self, response_file: Path) -> EvaluationResult: + """Evaluate a JSONL file of model responses and return score.""" + logger.info(f"Evaluating responses from {response_file}") + + # Load responses from file + responses = self._load_responses_from_file(response_file) + + if not responses: + raise ValueError(f"No valid responses found in {response_file}") + + # Extract model name from first response + model_name = responses[0].get("model_name", "unknown") + + # Load ground truth data + split = self._extract_split_from_filename(response_file) + samples_dict = {s.pmcid: s for s in self.data_loader.load_split(split)} + + # Evaluate each response + sample_scores = [] + failed_predictions = [] + + for response in responses: + pmcid = response.get("pmcid") + if pmcid not in samples_dict: + logger.warning(f"No ground truth found for PMCID: {pmcid}") + failed_predictions.append(pmcid) + continue + + try: + ground_truth = samples_dict[pmcid].annotation + prediction = {k: v for k, v in response.items() + if k not in ["model_name", "timestamp", "pmcid"]} + + sample_score = self.metrics.evaluate_sample(prediction, ground_truth, pmcid) + sample_scores.append(sample_score) + + except Exception as e: + logger.error(f"Error evaluating response for {pmcid}: {e}") + failed_predictions.append(pmcid) + + # Aggregate results + aggregate_metrics = self.metrics.aggregate_results(sample_scores) + error_analysis = self.metrics.generate_error_analysis(sample_scores) + + logger.info(f"Evaluation completed: {len(sample_scores)} successful, {len(failed_predictions)} failed") + + return EvaluationResult( + model_name=model_name, + sample_scores=sample_scores, + aggregate_metrics=aggregate_metrics, + error_analysis=error_analysis, + total_samples=len(responses), + successful_predictions=len(sample_scores), + failed_predictions=failed_predictions + ) + + def run_benchmark( + self, + model_configs: List[Dict[str, Any]], + split: str = "test" + ) -> Dict[str, EvaluationResult]: + """Run benchmark on multiple models (legacy method - generates and evaluates in one step).""" + logger.info(f"Starting benchmark on {split} split with {len(model_configs)} models") + + # Generate responses + response_files = self.generate_responses(model_configs, split) + + # Evaluate each response file + results = {} + for model_name, response_file in response_files.items(): + try: + result = self.evaluate_responses_file(response_file) + results[model_name] = result + + # Save intermediate results + self._save_intermediate_result(result, split) + + except Exception as e: + logger.error(f"Error evaluating model {model_name}: {e}") + continue + + # Generate comparative report + if results: + self.reporter.generate_comparative_report(results, split) + + logger.info(f"Benchmark completed for {len(results)} models") + return results + + def run_single_model( + self, + model_config: Dict[str, Any], + split: str = "test" + ) -> EvaluationResult: + """Run benchmark on a single model.""" + results = self.run_benchmark([model_config], split) + model_name = model_config.get("name", "unknown") + + if model_name not in results: + raise RuntimeError(f"Failed to evaluate model {model_name}") + + return results[model_name] + + def analyze_sample( + self, + pmcid: str, + model_config: Dict[str, Any], + split: str = "test" + ) -> Dict[str, Any]: + """Analyze a specific sample in detail.""" + logger.info(f"Analyzing sample {pmcid}") + + # Load sample + samples = self.data_loader.load_split(split) + sample = next((s for s in samples if s.pmcid == pmcid), None) + + if sample is None: + raise ValueError(f"Sample {pmcid} not found in {split} split") + + # Create model and evaluator + model = create_model(**model_config) + evaluator = BenchmarkEvaluator( + model=model, + metrics=self.metrics, + schema=self.schema, + categorical_fields=self.categorical_fields + ) + + # Get prediction and evaluation + prediction = evaluator.get_prediction_only(sample) + sample_score = evaluator.evaluate_single(sample) + + analysis = { + "pmcid": pmcid, + "article_title": sample.article_title, + "model": model_config.get("name", "unknown"), + "prediction": prediction, + "ground_truth": sample.annotation, + "scores": sample_score.__dict__ if sample_score else None, + "article_content_preview": sample.article_content[:1000] + "..." + } + + # Save detailed analysis + self.reporter.save_sample_analysis(analysis) + + return analysis + + def validate_setup(self) -> Dict[str, Any]: + """Validate the benchmark setup.""" + validation_report = { + "config_valid": True, + "data_available": True, + "models_accessible": {}, + "issues": [] + } + + try: + # Check data availability + train_samples = self.data_loader.load_split("train") + val_samples = self.data_loader.load_split("val") + test_samples = self.data_loader.load_split("test") + + validation_report["data_statistics"] = { + "train_samples": len(train_samples), + "val_samples": len(val_samples), + "test_samples": len(test_samples) + } + + if not train_samples: + validation_report["issues"].append("No training samples found") + validation_report["data_available"] = False + + except Exception as e: + validation_report["issues"].append(f"Data loading error: {e}") + validation_report["data_available"] = False + + # Test model configurations + test_configs = [ + {"name": "mock", "model_name": "mock"}, + {"name": "gpt-4", "model_name": "gpt-4"}, + {"name": "claude-3-sonnet", "model_name": "claude-3-sonnet-20240229"} + ] + + for config in test_configs: + try: + model = create_model(**config) + is_available = model.is_available() + validation_report["models_accessible"][config["name"]] = is_available + + if not is_available: + validation_report["issues"].append(f"Model {config['name']} not accessible") + + except Exception as e: + validation_report["models_accessible"][config["name"]] = False + validation_report["issues"].append(f"Error testing {config['name']}: {e}") + + # Overall validation + if validation_report["issues"]: + validation_report["config_valid"] = False + + return validation_report + + def _generate_model_responses( + self, + model: LanguageModelInterface, + samples: List[BenchmarkSample], + split: str + ) -> Path: + """Generate responses for all samples and save to JSONL file.""" + from .evaluator import BenchmarkEvaluator + + # Create evaluator for response generation + evaluator = BenchmarkEvaluator( + model=model, + metrics=self.metrics, + schema=self.schema, + categorical_fields=self.categorical_fields + ) + + # Create output file + timestamp = int(time.time()) + filename = f"{model.model_name}_{split}_{timestamp}.jsonl" + filepath = self.config.output_dir / "responses" / filename + filepath.parent.mkdir(parents=True, exist_ok=True) + + logger.info(f"Generating responses to {filepath}") + + with open(filepath, 'w') as f: + for i, sample in enumerate(samples): + logger.info(f"Processing sample {i+1}/{len(samples)}: {sample.pmcid}") + + try: + # Generate prediction + prediction = evaluator.get_prediction_only(sample) + + if prediction is None: + logger.warning(f"Failed to generate prediction for {sample.pmcid}") + continue + + # Add metadata + response_data = { + "pmcid": sample.pmcid, + "model_name": model.model_name, + "timestamp": time.time(), + **prediction + } + + # Write to JSONL file + f.write(json.dumps(response_data) + '\n') + + except Exception as e: + logger.error(f"Error processing sample {sample.pmcid}: {e}") + continue + + logger.info(f"Response generation completed: {filepath}") + return filepath + + def _load_responses_from_file(self, response_file: Path) -> List[Dict[str, Any]]: + """Load responses from a JSONL file.""" + responses = [] + + with open(response_file, 'r') as f: + for line_num, line in enumerate(f, 1): + line = line.strip() + if not line: + continue + + try: + response = json.loads(line) + responses.append(response) + except json.JSONDecodeError as e: + logger.warning(f"Invalid JSON on line {line_num} in {response_file}: {e}") + continue + + logger.info(f"Loaded {len(responses)} responses from {response_file}") + return responses + + def _extract_split_from_filename(self, filepath: Path) -> str: + """Extract split name from response filename.""" + filename = filepath.stem + parts = filename.split('_') + + # Filename format: {model_name}_{split}_{timestamp} + if len(parts) >= 2: + return parts[-2] # Second to last part should be split + + return "test" # Default fallback + + def _save_intermediate_result(self, result: EvaluationResult, split: str) -> None: + """Save intermediate result to file.""" + timestamp = int(time.time()) + filename = f"{result.model_name}_{split}_{timestamp}.json" + filepath = self.config.output_dir / "intermediate" / filename + + filepath.parent.mkdir(parents=True, exist_ok=True) + + # Convert result to serializable format + result_dict = { + "model_name": result.model_name, + "total_samples": result.total_samples, + "successful_predictions": result.successful_predictions, + "failed_predictions": result.failed_predictions, + "aggregate_metrics": result.aggregate_metrics, + "error_analysis": result.error_analysis, + "timestamp": timestamp + } + + with open(filepath, 'w') as f: + json.dump(result_dict, f, indent=2) + + logger.info(f"Intermediate result saved to {filepath}") + + def get_data_statistics(self, split: str = "train") -> Dict[str, Any]: + """Get comprehensive statistics about the data.""" + samples = self.data_loader.load_split(split) + return self.data_loader.get_statistics(samples) + + def export_predictions( + self, + result: EvaluationResult, + split: str, + format: str = "jsonl" + ) -> Path: + """Export model predictions for analysis.""" + return self.reporter.export_predictions(result, split, format) \ No newline at end of file diff --git a/src/benchmark/reporting.py b/src/benchmark/reporting.py new file mode 100644 index 0000000..a61ab0c --- /dev/null +++ b/src/benchmark/reporting.py @@ -0,0 +1,395 @@ +""" +Reporting and visualization utilities for benchmark results. +""" + +import json +import csv +from pathlib import Path +from typing import Dict, List, Any, Optional +from datetime import datetime +import logging + +from .evaluator import EvaluationResult + +logger = logging.getLogger(__name__) + + +class BenchmarkReporter: + """Generate reports and visualizations for benchmark results.""" + + def __init__(self, output_dir: Path): + self.output_dir = output_dir + self.output_dir.mkdir(parents=True, exist_ok=True) + + def generate_comparative_report( + self, + results: Dict[str, EvaluationResult], + split: str + ) -> Path: + """Generate a comparative report across multiple models.""" + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + report_dir = self.output_dir / f"comparative_report_{split}_{timestamp}" + report_dir.mkdir(parents=True, exist_ok=True) + + # Generate summary report + summary_path = self._generate_summary_report(results, report_dir, split) + + # Generate detailed reports for each model + for model_name, result in results.items(): + self._generate_model_report(result, report_dir / f"{model_name}_detailed.html") + + # Generate performance comparison + self._generate_performance_comparison(results, report_dir / "performance_comparison.html") + + # Export raw data + self._export_raw_results(results, report_dir) + + logger.info(f"Comparative report generated at {report_dir}") + return report_dir + + def _generate_summary_report( + self, + results: Dict[str, EvaluationResult], + report_dir: Path, + split: str + ) -> Path: + """Generate HTML summary report.""" + html_content = f""" + + + + Benchmark Summary Report - {split.upper()} + + + +
+

Pharmacogenomic Benchmark Results

+

Split: {split.upper()}

+

Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}

+

Models Evaluated: {len(results)}

+
+ +

Overall Performance Comparison

+ + + + + + + + + + + + +""" + + # Sort models by weighted score + sorted_results = sorted( + results.items(), + key=lambda x: x[1].aggregate_metrics.get("mean_weighted_score", 0), + reverse=True + ) + + for model_name, result in sorted_results: + metrics = result.aggregate_metrics + overall_score = metrics.get("mean_overall_score", 0) + weighted_score = metrics.get("mean_weighted_score", 0) + success_rate = result.successful_predictions / result.total_samples * 100 + + score_class = self._get_score_class(weighted_score) + + html_content += f""" + + + + + + + + +""" + + html_content += """ + +
ModelOverall ScoreWeighted ScoreSuccessful PredictionsFailed PredictionsSuccess Rate
{model_name}{overall_score:.3f}{weighted_score:.3f}{result.successful_predictions}{len(result.failed_predictions)}{success_rate:.1f}%
+ +

Field-Level Performance

+""" + + # Field-level comparison + if results: + # Get all fields from first result + first_result = next(iter(results.values())) + field_stats = first_result.aggregate_metrics.get("field_statistics", {}) + + html_content += """ + + + + +""" + for model_name in results.keys(): + html_content += f"" + html_content += f"" + + html_content += """ + + + +""" + + for field_name in sorted(field_stats.keys()): + html_content += f"" + + for model_name, result in results.items(): + field_data = result.aggregate_metrics.get("field_statistics", {}).get(field_name, {}) + mean_score = field_data.get("mean_score", 0) + exact_match_rate = field_data.get("exact_match_rate", 0) * 100 + + score_class = self._get_score_class(mean_score) + html_content += f'' + html_content += f'' + + html_content += "" + + html_content += """ + +
Field{model_name} (Score){model_name} (Exact Match %)
{field_name}{mean_score:.3f}{exact_match_rate:.1f}%
+""" + + # Model-specific sections + for model_name, result in sorted_results: + html_content += self._generate_model_section_html(model_name, result) + + html_content += """ + + +""" + + summary_path = report_dir / "summary.html" + with open(summary_path, 'w') as f: + f.write(html_content) + + return summary_path + + def _generate_model_section_html(self, model_name: str, result: EvaluationResult) -> str: + """Generate HTML section for a single model.""" + metrics = result.aggregate_metrics + + html = f""" +
+

{model_name}

+

Total Samples: {result.total_samples}

+

Successful Predictions: {result.successful_predictions}

+

Mean Overall Score: {metrics.get('mean_overall_score', 0):.3f}

+

Mean Weighted Score: {metrics.get('mean_weighted_score', 0):.3f}

+ +

Score Distribution

+ + +

Top Field Issues

+ +
+""" + + return html + + def _get_score_class(self, score: float) -> str: + """Get CSS class for score styling.""" + if score >= 0.8: + return "high-score" + elif score >= 0.5: + return "medium-score" + else: + return "low-score" + + def _generate_model_report(self, result: EvaluationResult, output_path: Path) -> None: + """Generate detailed report for a single model.""" + # For now, create a simple JSON report + # In a full implementation, this would be a rich HTML report + + report_data = { + "model_name": result.model_name, + "summary": { + "total_samples": result.total_samples, + "successful_predictions": result.successful_predictions, + "success_rate": result.successful_predictions / result.total_samples, + "aggregate_metrics": result.aggregate_metrics + }, + "error_analysis": result.error_analysis, + "failed_predictions": result.failed_predictions + } + + with open(output_path.with_suffix('.json'), 'w') as f: + json.dump(report_data, f, indent=2) + + def _generate_performance_comparison( + self, + results: Dict[str, EvaluationResult], + output_path: Path + ) -> None: + """Generate performance comparison visualization.""" + # Create a simple comparison table + # In a full implementation, this would include charts and graphs + + comparison_data = {} + for model_name, result in results.items(): + comparison_data[model_name] = { + "overall_score": result.aggregate_metrics.get("mean_overall_score", 0), + "weighted_score": result.aggregate_metrics.get("mean_weighted_score", 0), + "success_rate": result.successful_predictions / result.total_samples, + "field_performance": result.aggregate_metrics.get("field_statistics", {}) + } + + with open(output_path.with_suffix('.json'), 'w') as f: + json.dump(comparison_data, f, indent=2) + + def _export_raw_results(self, results: Dict[str, EvaluationResult], output_dir: Path) -> None: + """Export raw results in various formats.""" + raw_dir = output_dir / "raw_data" + raw_dir.mkdir(exist_ok=True) + + # Export as JSON + for model_name, result in results.items(): + # Sample-level scores + sample_scores_data = [] + for sample_score in result.sample_scores: + sample_data = { + "pmcid": sample_score.pmcid, + "overall_score": sample_score.overall_score, + "weighted_score": sample_score.weighted_score, + "field_scores": [ + { + "field": fs.field_name, + "score": fs.score, + "exact_match": fs.exact_match, + "predicted": fs.predicted, + "expected": fs.expected, + "error_type": fs.error_type + } + for fs in sample_score.field_scores + ] + } + sample_scores_data.append(sample_data) + + with open(raw_dir / f"{model_name}_sample_scores.json", 'w') as f: + json.dump(sample_scores_data, f, indent=2) + + def export_predictions( + self, + result: EvaluationResult, + split: str, + format: str = "jsonl" + ) -> Path: + """Export model predictions for external analysis.""" + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"{result.model_name}_{split}_predictions_{timestamp}" + + if format == "jsonl": + output_path = self.output_dir / f"{filename}.jsonl" + with open(output_path, 'w') as f: + for sample_score in result.sample_scores: + prediction_data = { + "pmcid": sample_score.pmcid, + "overall_score": sample_score.overall_score, + "predictions": { + fs.field_name: fs.predicted for fs in sample_score.field_scores + }, + "ground_truth": { + fs.field_name: fs.expected for fs in sample_score.field_scores + } + } + f.write(json.dumps(prediction_data) + '\n') + + elif format == "csv": + output_path = self.output_dir / f"{filename}.csv" + + if result.sample_scores: + # Get all field names + field_names = list(set( + fs.field_name for sample_score in result.sample_scores + for fs in sample_score.field_scores + )) + field_names.sort() + + with open(output_path, 'w', newline='') as f: + writer = csv.writer(f) + + # Header + header = ['pmcid', 'overall_score', 'weighted_score'] + for field in field_names: + header.extend([f"{field}_predicted", f"{field}_expected", f"{field}_score"]) + writer.writerow(header) + + # Data rows + for sample_score in result.sample_scores: + row = [sample_score.pmcid, sample_score.overall_score, sample_score.weighted_score] + + # Create field lookup + field_lookup = {fs.field_name: fs for fs in sample_score.field_scores} + + for field in field_names: + if field in field_lookup: + fs = field_lookup[field] + row.extend([fs.predicted, fs.expected, fs.score]) + else: + row.extend([None, None, 0.0]) + + writer.writerow(row) + + else: + raise ValueError(f"Unsupported export format: {format}") + + logger.info(f"Predictions exported to {output_path}") + return output_path + + def save_sample_analysis(self, analysis: Dict[str, Any]) -> Path: + """Save detailed analysis of a single sample.""" + pmcid = analysis["pmcid"] + model = analysis["model"] + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + + filename = f"sample_analysis_{pmcid}_{model}_{timestamp}.json" + output_path = self.output_dir / "sample_analyses" / filename + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, 'w') as f: + json.dump(analysis, f, indent=2) + + logger.info(f"Sample analysis saved to {output_path}") + return output_path \ No newline at end of file From ebbcb09b47346781e6f42e71e1b52be5023658f5 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Fri, 6 Jun 2025 12:53:53 -0700 Subject: [PATCH 02/28] fix: serialization error --- .gitignore | 3 +++ benchmark_example.py | 23 ++++++++++------------- src/benchmark/data_loader.py | 3 +-- src/benchmark/evaluator.py | 3 +-- src/benchmark/metrics.py | 23 +++++++++++++++++++++-- src/benchmark/models.py | 7 +++++-- src/benchmark/pipeline.py | 26 ++++++++++++++------------ src/benchmark/reporting.py | 4 +--- 8 files changed, 56 insertions(+), 36 deletions(-) diff --git a/.gitignore b/.gitignore index 9dd7b0d..aeaf421 100644 --- a/.gitignore +++ b/.gitignore @@ -36,3 +36,6 @@ data/markdown .DS_Store +*.log + +benchmark_results/ \ No newline at end of file diff --git a/benchmark_example.py b/benchmark_example.py index 402fdfe..cd8098b 100644 --- a/benchmark_example.py +++ b/benchmark_example.py @@ -22,19 +22,13 @@ """ import os -import logging from pathlib import Path - +from loguru import logger from src.benchmark import BenchmarkPipeline, BenchmarkConfig +from dotenv import load_dotenv -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' -) - -logger = logging.getLogger(__name__) - +load_dotenv() +logger.add("benchmark_example.log", rotation="10 MB") def main(): """Main example demonstrating benchmark usage.""" @@ -45,7 +39,7 @@ def main(): articles_dir=Path("data/articles"), benchmark_dir=Path("data/benchmark"), output_dir=Path("benchmark_results"), - model_name="mock", # Start with mock model for testing + model_name="claude-3-sonnet", # Start with mock model for testing max_articles=10, # Limit for example batch_size=5 ) @@ -75,8 +69,11 @@ def main(): # 5. Run benchmark on test models model_configs = [ { - "name": "mock_model", - "model_name": "mock" + "name": "claude-3-sonnet", + "model_name": "claude-3-sonnet-20240229", + "api_key": os.getenv("ANTHROPIC_API_KEY"), + "temperature": 0.0, + "max_tokens": 4000 } ] diff --git a/src/benchmark/data_loader.py b/src/benchmark/data_loader.py index 4996aac..d5c22d6 100644 --- a/src/benchmark/data_loader.py +++ b/src/benchmark/data_loader.py @@ -7,9 +7,8 @@ from pathlib import Path from typing import Dict, List, Optional, Tuple, Any from dataclasses import dataclass -import logging -logger = logging.getLogger(__name__) +from loguru import logger @dataclass diff --git a/src/benchmark/evaluator.py b/src/benchmark/evaluator.py index f2f9dbd..92c3475 100644 --- a/src/benchmark/evaluator.py +++ b/src/benchmark/evaluator.py @@ -6,13 +6,12 @@ import re from typing import Dict, List, Any, Optional from dataclasses import dataclass -import logging +from loguru import logger from .data_loader import BenchmarkSample from .metrics import EvaluationMetrics, SampleScore from .models import LanguageModelInterface, PharmacogenomicPromptBuilder -logger = logging.getLogger(__name__) @dataclass diff --git a/src/benchmark/metrics.py b/src/benchmark/metrics.py index 5d29008..89b0b38 100644 --- a/src/benchmark/metrics.py +++ b/src/benchmark/metrics.py @@ -7,9 +7,8 @@ from typing import Dict, List, Any, Tuple, Optional, Union from dataclasses import dataclass from difflib import SequenceMatcher -import logging +from loguru import logger -logger = logging.getLogger(__name__) @dataclass @@ -21,6 +20,17 @@ class FieldScore: predicted: Any expected: Any error_type: Optional[str] = None + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for JSON serialization.""" + return { + 'field_name': self.field_name, + 'exact_match': self.exact_match, + 'score': self.score, + 'predicted': self.predicted, + 'expected': self.expected, + 'error_type': self.error_type + } @dataclass @@ -30,6 +40,15 @@ class SampleScore: field_scores: List[FieldScore] overall_score: float weighted_score: float + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for JSON serialization.""" + return { + 'pmcid': self.pmcid, + 'field_scores': [fs.to_dict() for fs in self.field_scores], + 'overall_score': self.overall_score, + 'weighted_score': self.weighted_score + } class EvaluationMetrics: diff --git a/src/benchmark/models.py b/src/benchmark/models.py index 9b06106..0340e96 100644 --- a/src/benchmark/models.py +++ b/src/benchmark/models.py @@ -7,9 +7,8 @@ from abc import ABC, abstractmethod from typing import Dict, Any, List, Optional from dataclasses import dataclass -import logging +from loguru import logger -logger = logging.getLogger(__name__) @dataclass @@ -79,6 +78,8 @@ def generate(self, prompt: str, **kwargs) -> ModelResponse: def is_available(self) -> bool: """Check if OpenAI API is available.""" + if not self.api_key: + return False try: self.client.models.list() return True @@ -128,6 +129,8 @@ def generate(self, prompt: str, **kwargs) -> ModelResponse: def is_available(self) -> bool: """Check if Anthropic API is available.""" + if not self.api_key: + return False try: # Simple test call self.client.messages.create( diff --git a/src/benchmark/pipeline.py b/src/benchmark/pipeline.py index f8ec09a..8febfd5 100644 --- a/src/benchmark/pipeline.py +++ b/src/benchmark/pipeline.py @@ -6,7 +6,8 @@ import time from pathlib import Path from typing import Dict, List, Any, Optional -import logging +from loguru import logger + from .config import BenchmarkConfig from .data_loader import BenchmarkDataLoader, BenchmarkSample @@ -15,9 +16,6 @@ from .evaluator import BenchmarkEvaluator, EvaluationResult from .reporting import BenchmarkReporter -logger = logging.getLogger(__name__) - - class BenchmarkPipeline: """Main pipeline for running benchmarks.""" @@ -217,7 +215,7 @@ def analyze_sample( "model": model_config.get("name", "unknown"), "prediction": prediction, "ground_truth": sample.annotation, - "scores": sample_score.__dict__ if sample_score else None, + "scores": sample_score.to_dict() if sample_score else None, "article_content_preview": sample.article_content[:1000] + "..." } @@ -255,12 +253,16 @@ def validate_setup(self) -> Dict[str, Any]: validation_report["issues"].append(f"Data loading error: {e}") validation_report["data_available"] = False - # Test model configurations - test_configs = [ - {"name": "mock", "model_name": "mock"}, - {"name": "gpt-4", "model_name": "gpt-4"}, - {"name": "claude-3-sonnet", "model_name": "claude-3-sonnet-20240229"} - ] + # Test model configurations - only test models with available API keys + import os + test_configs = [{"name": "mock", "model_name": "mock"}] + + # Only test models if their API keys are available + if os.getenv("OPENAI_API_KEY"): + test_configs.append({"name": "gpt-4", "model_name": "gpt-4", "api_key": os.getenv("OPENAI_API_KEY")}) + + if os.getenv("ANTHROPIC_API_KEY"): + test_configs.append({"name": "claude-3-sonnet", "model_name": "claude-3-sonnet-20240229", "api_key": os.getenv("ANTHROPIC_API_KEY")}) for config in test_configs: try: @@ -268,7 +270,7 @@ def validate_setup(self) -> Dict[str, Any]: is_available = model.is_available() validation_report["models_accessible"][config["name"]] = is_available - if not is_available: + if not is_available and config["name"] != "mock": validation_report["issues"].append(f"Model {config['name']} not accessible") except Exception as e: diff --git a/src/benchmark/reporting.py b/src/benchmark/reporting.py index a61ab0c..ca1145c 100644 --- a/src/benchmark/reporting.py +++ b/src/benchmark/reporting.py @@ -7,12 +7,10 @@ from pathlib import Path from typing import Dict, List, Any, Optional from datetime import datetime -import logging +from loguru import logger from .evaluator import EvaluationResult -logger = logging.getLogger(__name__) - class BenchmarkReporter: """Generate reports and visualizations for benchmark results.""" From 29daadb9f452b6963e7eb685d0fec83f07328173 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Sat, 7 Jun 2025 15:14:48 -0700 Subject: [PATCH 03/28] feat: moved variant downloading to PubMedDownloader --- src/fetch_articles/README.md | 161 ----------------- src/fetch_articles/__init__.py | 0 src/fetch_articles/article_downloader.py | 118 ------------ src/fetch_articles/pmcid_converter.py | 190 -------------------- src/load_variants/README.md | 30 ---- src/load_variants/__init__.py | 5 - src/load_variants/load_clinical_variants.py | 136 -------------- src/utils/__init__.py | 1 - src/utils/file_paths.py | 11 -- 9 files changed, 652 deletions(-) delete mode 100644 src/fetch_articles/README.md delete mode 100644 src/fetch_articles/__init__.py delete mode 100644 src/fetch_articles/article_downloader.py delete mode 100644 src/fetch_articles/pmcid_converter.py delete mode 100644 src/load_variants/README.md delete mode 100644 src/load_variants/__init__.py delete mode 100644 src/load_variants/load_clinical_variants.py delete mode 100644 src/utils/__init__.py delete mode 100644 src/utils/file_paths.py diff --git a/src/fetch_articles/README.md b/src/fetch_articles/README.md deleted file mode 100644 index 19503ef..0000000 --- a/src/fetch_articles/README.md +++ /dev/null @@ -1,161 +0,0 @@ -# PubMed Document Fetching - -## Goal -Given a PMID, fetch the paper from PubMed. Ignore papers where there are paywall issues. - -## Process Overview -1. Download the zip of variants from pharmgkb (handled in load_variants module) -2. Get a PMID list from the variants tsv (column PMID) (handled in load_variants module) -3. Convert the PMID to PMCID -4. Fetch the content from the PMCID - -## Key Functions - -### PMCID Converter (`pmcid_converter.py`) - -- `batch_pmid_to_pmcid(pmids, email, batch_size, delay)`: Converts a list of PMIDs to PMCIDs using NCBI's ID Converter API. Processes PMIDs in batches and handles rate limiting. - - Arguments: - - `pmids`: List of PMIDs (as strings) - - `email`: Your email for NCBI tool identification - - `batch_size`: Number of PMIDs per request (max: 200) - - `delay`: Seconds between requests (default: 0.4) - - Returns: Dict mapping each PMID to PMCID (or None if not available) - -- `get_unique_pmcids()`: Returns a list of unique PMCIDs from the PMCID mapping file. - -- `load_saved_pmcid_mapping()`: Loads previously saved PMCID mappings from disk. - -- `get_project_root()`: Returns the project root directory path. - -### Article Downloader (`article_downloader.py`) - -- `fetch_pmc_content(pmcid)`: Fetches a single article's content from PubMed Central. - - Arguments: - - `pmcid`: The PubMed Central ID to fetch - - Returns: Article content in XML format or None if fetching failed - -- `download_articles(pmcids)`: Downloads multiple articles from PubMed Central. - - Arguments: - - `pmcids`: List of PMCIDs to download - - Saves downloaded articles to `data/articles/` as XML files - - Tracks downloaded PMCIDs to avoid duplicating work - -- `update_downloaded_pmcids()`: Updates tracking of downloaded PMCIDs from files in `data/articles/` directory. - -## Created Data -- `pmcid_mapping.json`: Maps the PMID to the PMCID `{"PMID": "PMCID" or Null, ..}` -- `unique_pmcids.json`: List of all the unique PMCIDs from pmcid_mapping.json `["PMCID1", "PMCID2", ...]` -- `downloaded_pmcids.json`: Maps PMCIDs to filenames or None if download failed `{"PMCID": "PMCID.xml" or null, ..}` -- `.xml`: Downloaded articles - -## Usage Examples - -### Convert PMIDs to PMCIDs - -```python -from src.fetch_articles.pmcid_converter import batch_pmid_to_pmcid -from src.load_variants import get_pmid_list -import os -from dotenv import load_dotenv - -load_dotenv() # Load environment variables (NCBI_EMAIL) - -# Get list of PMIDs from variant data -pmid_list = get_pmid_list() - -# Convert PMIDs to PMCIDs -pmcid_mapping = batch_pmid_to_pmcid( - pmids=pmid_list, - email=os.getenv("NCBI_EMAIL"), - batch_size=100, - delay=0.4 -) - -print(f"Successfully mapped {len(pmcid_mapping)} PMIDs to PMCIDs") -``` - -### Download Articles Using PMCIDs - -```python -from src.fetch_articles.article_downloader import download_articles -from src.fetch_articles.pmcid_converter import get_unique_pmcids - -# Get unique PMCIDs from saved mapping -pmcids = get_unique_pmcids() - -# Download articles -download_articles(pmcids) -``` - -### Download a Single Article - -```python -from src.fetch_articles.article_downloader import fetch_pmc_content -from src.fetch_articles.pmcid_converter import get_project_root -import os -from pathlib import Path - -# Get project root -project_root = get_project_root() - -# Fetch a single article -pmcid = "PMC1234567" -content = fetch_pmc_content(pmcid) - -if content: - # Save the article content - articles_dir = project_root / "data" / "articles" - os.makedirs(articles_dir, exist_ok=True) - - with open(articles_dir / f"{pmcid}.xml", "w") as f: - f.write(content.decode("utf-8")) - print(f"Successfully downloaded article {pmcid}") -else: - print(f"Failed to download article {pmcid}") -``` - -### Update Downloaded PMCIDs - -```python -from src.fetch_articles.article_downloader import update_downloaded_pmcids - -# Update downloaded_pmcids.json with articles in data/articles/ -update_downloaded_pmcids() -``` - -## Full Pipeline Execution - -To run the complete pipeline (convert PMIDs to PMCIDs and download articles): - -```python -# Full pipeline from PMIDs to downloaded articles -from src.fetch_articles.pmcid_converter import batch_pmid_to_pmcid -from src.fetch_articles.article_downloader import download_articles -from src.load_variants import get_pmid_list -import os -from dotenv import load_dotenv - -load_dotenv() - -# 1. Get PMIDs from variant data -pmid_list = get_pmid_list() - -# 2. Convert PMIDs to PMCIDs -pmcid_mapping = batch_pmid_to_pmcid(pmid_list, os.getenv("NCBI_EMAIL")) - -# 3. Extract only valid PMCIDs (not None) -valid_pmcids = [pmcid for pmcid in pmcid_mapping.values() if pmcid] - -# 4. Download articles -download_articles(valid_pmcids) -``` - -Alternatively, run the module scripts directly: - -```bash -# First convert PMIDs to PMCIDs -python -m src.fetch_articles.pmcid_converter - -# Then download articles -python -m src.fetch_articles.article_downloader -``` \ No newline at end of file diff --git a/src/fetch_articles/__init__.py b/src/fetch_articles/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/fetch_articles/article_downloader.py b/src/fetch_articles/article_downloader.py deleted file mode 100644 index 543f01e..0000000 --- a/src/fetch_articles/article_downloader.py +++ /dev/null @@ -1,118 +0,0 @@ -from loguru import logger -from src.fetch_articles.pmcid_converter import get_unique_pmcids -from src.utils.file_paths import get_project_root -from Bio import Entrez -import os -import json -from tqdm import tqdm - - -def fetch_pmc_content(pmcid): - """ - Fetch content for a single article from PubMed Central. - - Args: - pmcid (str): The PubMed Central ID to fetch - - Returns: - bytes or None: The article content in XML format or None if fetching failed - """ - try: - handle = Entrez.efetch(db="pmc", id=pmcid, rettype="full", retmode="xml") - record = handle.read() - handle.close() - return record - except Exception as e: - print(f"An error occurred while fetching content for PMCID {pmcid}: {e}") - return None - - -def update_downloaded_pmcids() -> None: - """ - Update the downloaded_pmcids.json file with PMCIDs found in the data/articles directory. - """ - project_root = get_project_root() - downloaded_pmcids_path = project_root / "data" / "downloaded_pmcids.json" - - # Check for all the filenames in the data/articles directory - articles_dir = project_root / "data" / "articles" - os.makedirs(articles_dir, exist_ok=True) - - article_pmcids = [f.split(".")[0] for f in os.listdir(articles_dir)] - article_pmcids_mapping = {pmcid: f"{pmcid}.xml" for pmcid in article_pmcids} - - logger.info(f"Found {len(article_pmcids)} existing XML files in {articles_dir}") - - # Add the new PMCIDs to the json file - if os.path.exists(downloaded_pmcids_path): - with open(downloaded_pmcids_path, "r") as f: - try: - downloaded_pmcids = json.load(f) - except json.JSONDecodeError: - logger.error( - f"Error loading {downloaded_pmcids_path}. Creating new json file." - ) - downloaded_pmcids = {} - else: - downloaded_pmcids = {} - - downloaded_pmcids.update(article_pmcids_mapping) - - with open(downloaded_pmcids_path, "w") as f: - json.dump(downloaded_pmcids, f) - - logger.info( - f"Updated {downloaded_pmcids_path} with {len(article_pmcids)} new PMCIDs" - ) - - -def download_articles(pmcids: list[str]): - """ - Download articles from PubMed Central using PMCIDs. - Keeps track of the PMCIDs that have been downloaded and skips them. - Saves the downloaded articles to the data/articles directory. - - Args: - pmcids (list[str]): List of PMCIDs to download. - """ - project_root = get_project_root() - articles_dir = project_root / "data" / "articles" - os.makedirs(articles_dir, exist_ok=True) - - # Load the downloaded PMCIDs from the json file - downloaded_pmcids_path = project_root / "data" / "downloaded_pmcids.json" - - if os.path.exists(downloaded_pmcids_path): - with open(downloaded_pmcids_path, "r") as f: - downloaded_pmcids = json.load(f) - else: - downloaded_pmcids = {} - - new_pmcids = [pmcid for pmcid in pmcids if pmcid not in downloaded_pmcids] - logger.warning(f"{len(downloaded_pmcids)} existing articles found") - logger.info(f"{len(new_pmcids)} new articles to download") - - # Download the articles - for pmcid in tqdm(new_pmcids): - record = fetch_pmc_content(pmcid) - if record: - with open(articles_dir / f"{pmcid}.xml", "w") as f: - f.write(record.decode("utf-8")) - downloaded_pmcids[pmcid] = f"{pmcid}.xml" - else: - downloaded_pmcids[pmcid] = None - logger.warning(f"No record found for PMCID {pmcid}") - - logger.info( - f"Downloaded {len(new_pmcids)} new articles, total articles: {len(downloaded_pmcids)}" - ) - - # Save the downloaded PMCIDs to a json file - with open(downloaded_pmcids_path, "w") as f: - json.dump(downloaded_pmcids, f) - - -if __name__ == "__main__": - update_downloaded_pmcids() - pmcids = get_unique_pmcids() - download_articles(pmcids) diff --git a/src/fetch_articles/pmcid_converter.py b/src/fetch_articles/pmcid_converter.py deleted file mode 100644 index 417ec57..0000000 --- a/src/fetch_articles/pmcid_converter.py +++ /dev/null @@ -1,190 +0,0 @@ -import time -import random -import pandas as pd -from Bio import Entrez -from tqdm import tqdm -from dotenv import load_dotenv -import os -from src.load_variants import get_pmid_list -import json -from src.utils.file_paths import get_project_root - -load_dotenv() -# Email for NCBI -Entrez.email = os.getenv("NCBI_EMAIL") - -# Step 1: Function to get PMCID from PMID -import requests -from loguru import logger - -import requests -import time -from loguru import logger -from typing import List, Set, Dict, Optional - - -def load_saved_pmcid_mapping() -> Dict[str, Optional[str]]: - """ - Load the saved PMCID mapping from the json file. - """ - project_root = get_project_root() - results_path = project_root / "data" / "pmcid_mapping.json" - - # Create data directory if it doesn't exist - os.makedirs(project_root / "data", exist_ok=True) - - if os.path.exists(results_path): - with open(results_path, "r") as f: - existing_results = json.load(f) - logger.info( - f"Loaded {len(existing_results)} existing PMCID mappings from {results_path}" - ) - else: - logger.info( - f"No PMCID mapping found at {results_path}. Creating empty mapping." - ) - existing_results = {} - return existing_results - - -def batch_pmid_to_pmcid( - pmids: List[str], - email: str = os.getenv("NCBI_EMAIL"), - batch_size: int = 100, - delay: float = 0.4, -) -> Dict[str, Optional[str]]: - """ - Convert a list of PMIDs to PMCIDs using NCBI's ID Converter API. - - Args: - pmids: List of PMIDs (as strings). - email: Your email address for NCBI tool identification. - batch_size: Number of PMIDs to send per request (max: 200). - delay: Seconds to wait between requests (default 0.4 to respect NCBI). - - Returns: - Dict mapping each PMID to a PMCID (or None if not available). - """ - url = "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/" - results = {} - existing_results = load_saved_pmcid_mapping() - - # Check for existing results - existing_pmids = set(existing_results.keys()) - - # Remove existing results from pmids - filtered_pmids = [x for x in pmids if str(x) not in existing_pmids] - - logger.info(f"Remaining PMIDs to process: {len(filtered_pmids)}") - if len(filtered_pmids) == 0: - logger.warning("No PMIDs to process. Exiting.") - return existing_results - - # Process remaining PMIDs - for i in range(0, len(filtered_pmids), batch_size): - batch = filtered_pmids[i : i + batch_size] - batch_str = [str(pmid) for pmid in batch] - ids_str = ",".join(batch_str) - logger.info(f"Processing PMIDs {i + 1} to {i + len(batch)}...") - - params = { - "tool": "pmid2pmcid_tool", - "email": email, - "ids": ids_str, - "format": "json", - } - - try: - response = requests.get(url, params=params) - response.raise_for_status() - data = response.json() - records = data.get("records", []) - for record in records: - pmid = record.get("pmid") - pmcid = record.get("pmcid") - results[pmid] = pmcid if pmcid else None - if pmcid: - logger.info(f"PMID {pmid} → PMCID {pmcid}") - else: - logger.warning(f"PMID {pmid} has no PMCID available.") - except Exception as e: - logger.error(f"Failed batch starting at index {i}: {e}") - for pmid in batch: - results[pmid] = None - - time.sleep(delay) - - # Merge existing results with new results - existing_results.update(results) - - # Save updated results - project_root = get_project_root() - results_path = project_root / "data" / "pmcid_mapping.json" - - # Create data directory if it doesn't exist - os.makedirs(project_root / "data", exist_ok=True) - - with open(results_path, "w") as f: - json.dump(existing_results, f) - logger.info(f"Updated PMCID mappings saved to {results_path}") - - return existing_results - - -def get_unique_pmcids() -> List[str]: - """ - Get a list of unique PMCIDs from the PMCID mapping (pmcid_mapping.json) - NOTE: Could add functionality to check for new PMCIDs in mapping and update the unique_pmcids.json file - Currently function returns the pre-existing unique PMCIDs if they exist or regenerates the list from the mapping. - """ - project_root = get_project_root() - - # Load the unique PMCIDs if they've already been saved - unique_pmcids_path = project_root / "data" / "unique_pmcids.json" - - # Create data directory if it doesn't exist - os.makedirs(project_root / "data", exist_ok=True) - - if os.path.exists(unique_pmcids_path): - with open(unique_pmcids_path, "r") as f: - try: - pmcids = json.load(f) - except json.JSONDecodeError as e: - logger.error( - f"Error loading unique PMCIDs from {unique_pmcids_path}: {e}" - ) - raise e - logger.warning( - f"Loaded {len(pmcids)} pre-existing unique PMCIDs from {unique_pmcids_path}" - ) - return pmcids - - # Load from pmcid_mapping.json if unique pmcids haven't been saved - results_path = project_root / "data" / "pmcid_mapping.json" - - if not os.path.exists(results_path): - logger.error( - f"No PMCID mapping found at {results_path}. Cannot generate unique PMCIDs." - ) - return [] - - with open(results_path, "r") as f: - existing_results = json.load(f) - - # Get the unique pmcids (remove None values) - pmcids = [value for value in existing_results.values() if value is not None] - pmcids = list(set(pmcids)) - - # Save the unique pmcids to a json file - with open(unique_pmcids_path, "w") as f: - json.dump(pmcids, f) - logger.info(f"Unique PMCIDs saved to {unique_pmcids_path}") - return pmcids - - -if __name__ == "__main__": - pmid_list = get_pmid_list() - results = batch_pmid_to_pmcid(pmid_list, os.getenv("NCBI_EMAIL")) - logger.info(f"PMCID mapping complete. {len(results)} PMIDs mapped to PMCIDs.") - pmcids = get_unique_pmcids() - logger.info(f"Number of unique PMCIDs: {len(pmcids)}") diff --git a/src/load_variants/README.md b/src/load_variants/README.md deleted file mode 100644 index 048697e..0000000 --- a/src/load_variants/README.md +++ /dev/null @@ -1,30 +0,0 @@ -# Load Data Module - -This module handles the loading and preprocessing of PharmGKB clinical variants data. -TODO: Update this to use PubMedDownloader - -## Methods - -1. **`download_and_extract_variant_annotations(override: bool = False)`** - - Downloads and extracts the variant annotations ZIP file from PharmGKB - - Saves data to `data/variantAnnotations/` - - Can override existing downloads if needed - -2. **`load_raw_variant_annotations(override: bool = False)`** - - Loads the variant annotations TSV file into a pandas DataFrame - - Automatically downloads data if not present - - Returns the DataFrame containing variant-drug annotations - -3. **`unique_variants(df: pd.DataFrame)`** - - Helper function that generates a dictionary of unique values for each column - - Used for data analysis and validation - -4. **`get_pmid_list(override: bool = False)`** - - Main function to extract PMIDs from the variant annotations - - Returns a list of unique PMIDs - - Caches results in `data/pmid_list.json` - - Used as input for PMCID conversion - -The module handles all data downloading, extraction, and preprocessing steps needed to get the PMID list for subsequent steps in the pipeline. - - diff --git a/src/load_variants/__init__.py b/src/load_variants/__init__.py deleted file mode 100644 index 6c56850..0000000 --- a/src/load_variants/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .load_clinical_variants import ( - load_raw_variant_annotations, - get_pmid_list, - variant_annotations_pipeline, -) diff --git a/src/load_variants/load_clinical_variants.py b/src/load_variants/load_clinical_variants.py deleted file mode 100644 index c43b340..0000000 --- a/src/load_variants/load_clinical_variants.py +++ /dev/null @@ -1,136 +0,0 @@ -import os -import requests -import zipfile -from io import BytesIO -import shutil -from loguru import logger -import pandas as pd -import json - -from src.utils.file_paths import get_project_root - -""" -This file contains functions to load the clinical variants data from the PharmGKB API. -The key function is get_pmid_list(), which loads the PMIDs from the variant annotations tsv file and saves them to a json file. -""" - - -def download_and_extract_variant_annotations(override: bool = False) -> str: - """ - Downloads and extracts the variant annotations zip file. - If the folder already exists, it will be skipped unless override parameter is set to True. - Params: - override (bool): If True, the folder will be deleted and the zip file will be downloaded and extracted again. - Returns: - str: The path to the extracted folder. - """ - url = "https://api.pharmgkb.org/v1/download/file/data/variantAnnotations.zip" - - save_dir = os.path.join(get_project_root(), "data") - extract_dir = os.path.join(save_dir, "variantAnnotations") - - if os.path.exists(extract_dir): - if not override: - logger.info(f"Folder already exists at {extract_dir}. Skipping download.") - return extract_dir - else: - shutil.rmtree(extract_dir) - - os.makedirs(extract_dir, exist_ok=True) - - logger.info(f"Downloading ZIP from {url}...") - response = requests.get(url) - response.raise_for_status() - - logger.info("Extracting ZIP...") - with zipfile.ZipFile(BytesIO(response.content)) as z: - z.extractall(extract_dir) - - logger.info(f"Files extracted to: {extract_dir}") - return extract_dir - - -def load_raw_variant_annotations(override: bool = False) -> pd.DataFrame: - """ - Loads the variant annotations tsv file. - If the file does not exist, it will be downloaded and extracted. - Params: - override (bool): If True, the file will be downloaded and extracted again. - Returns: - pd.DataFrame: The loaded variant annotations tsv file. - """ - tsv_path = os.path.join( - get_project_root(), "data", "variantAnnotations", "var_drug_ann.tsv" - ) - - if not os.path.exists(tsv_path): - logger.info(f"{tsv_path} not found. Downloading data...") - download_and_extract_variant_annotations(override) - - if not os.path.exists(tsv_path): - logger.error(f"File still not found after download attempt: {tsv_path}") - raise FileNotFoundError( - f"File still not found after download attempt: {tsv_path}" - ) - - logger.info(f"Loading TSV from: {tsv_path}") - df = pd.read_csv(tsv_path, sep="\t") - return df - - -def unique_variants(df: pd.DataFrame) -> dict: - """ - Generates a dictionary with unique values for each column of a Pandas DataFrame. - - Args: - df: The input Pandas DataFrame. - - Returns: - A dictionary where keys are column names and values are lists of unique values - for that column. Returns an empty dictionary if the input is invalid. - """ - if not isinstance(df, pd.DataFrame): - logger.error("Input is not a Pandas DataFrame") - return {} - - return {col: df[col].unique().tolist() for col in df.columns} - - -def get_pmid_list(override: bool = False) -> list: - """ - Loads the pmid list from the variant annotations tsv file. - """ - pmid_list_path = os.path.join(get_project_root(), "data", "pmid_list.json") - if os.path.exists(pmid_list_path): - logger.info(f"Loading PMIDs from {pmid_list_path}") - with open(pmid_list_path, "r") as f: - pmid_list = json.load(f) - else: - df = load_raw_variant_annotations(override) - pmid_list = df["PMID"].unique().tolist() - logger.info(f"Saving PMIDs to {pmid_list_path}") - with open(pmid_list_path, "w") as f: - json.dump(pmid_list, f) - return pmid_list - - -def variant_annotations_pipeline(): - """ - Loads the variant annotations tsv file and saves the unique PMIDs to a json file. - """ - # Download and extract the variant annotations - logger.info("Downloading and extracting variant annotations...") - download_and_extract_variant_annotations() - - # Load the variant annotations - logger.info("Loading variant annotations...") - df = load_raw_variant_annotations() - - # Get the PMIDs - logger.info("Getting PMIDs...") - pmid_list = get_pmid_list() - logger.info(f"Number of unique PMIDs: {len(pmid_list)}") - - -if __name__ == "__main__": - variant_annotations_pipeline() diff --git a/src/utils/__init__.py b/src/utils/__init__.py deleted file mode 100644 index 4deed16..0000000 --- a/src/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .file_paths import get_project_root diff --git a/src/utils/file_paths.py b/src/utils/file_paths.py deleted file mode 100644 index 229c652..0000000 --- a/src/utils/file_paths.py +++ /dev/null @@ -1,11 +0,0 @@ -import os -from pathlib import Path - - -def get_project_root() -> Path: - """ - Return the project root directory. - """ - # Assuming src is a top-level directory in the project - current_file = Path(__file__) - return current_file.parent.parent.parent From 10462bef04e918f60b0b28197973c2fdb48bd1fb Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Mon, 9 Jun 2025 12:55:05 -0700 Subject: [PATCH 04/28] checkpoint: notebooks --- notebooks/create_pmid_annotations.ipynb | 433 ++++++++++++++++++++++++ notebooks/update_pmcid_mapping.ipynb | 22 ++ 2 files changed, 455 insertions(+) create mode 100644 notebooks/create_pmid_annotations.ipynb create mode 100644 notebooks/update_pmcid_mapping.ipynb diff --git a/notebooks/create_pmid_annotations.ipynb b/notebooks/create_pmid_annotations.ipynb new file mode 100644 index 0000000..4fc25d8 --- /dev/null +++ b/notebooks/create_pmid_annotations.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# Run this cell: \n", + "# The lines below will instruct jupyter to reload imported modules before \n", + "# executing code cells. This enables you to quickly iterate and test revisions\n", + "# to your code without having to restart the kernel and reload all of your \n", + "# modules each time you make a code change in a separate python file.\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "from tqdm import tqdm\n", + "import pickle\n", + "from loguru import logger\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB\n" + ] + } + ], + "source": [ + "# Change path to project root\n", + "if os.getcwd().endswith(\"notebooks\"):\n", + " os.chdir(os.path.dirname(os.getcwd()))\n", + "print(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# load all the data/variant_annotations tsv files\n", + "study_parameters = pd.read_csv(os.path.join(\"data\", \"variantAnnotations\", \"study_parameters.tsv\"), sep=\"\\t\")\n", + "var_drug_ann = pd.read_csv(os.path.join(\"data\", \"variantAnnotations\", \"var_drug_ann.tsv\"), sep=\"\\t\")\n", + "var_fa_ann = pd.read_csv(os.path.join(\"data\", \"variantAnnotations\", \"var_fa_ann.tsv\"), sep=\"\\t\")\n", + "var_pheno_ann = pd.read_csv(os.path.join(\"data\", \"variantAnnotations\", \"var_pheno_ann.tsv\"), sep=\"\\t\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Check unique Varriant Annotatiosn in study_parameters\n", + "unique_variant_annotations = study_parameters['Variant Annotation ID'].unique()\n", + "\n", + "# Find rows where a Variant Annotation ID appears in multiple rows\n", + "duplicate_variant_annotations = study_parameters[study_parameters.duplicated(subset=['Variant Annotation ID'])]\n", + "\n", + "# Count the number of duplicate Variant Annotation IDs\n", + "num_duplicate_variant_annotations = len(duplicate_variant_annotations)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['case/control', 'meta-analysis', 'cohort', nan, 'retrospective',\n", + " 'case series', 'clinical trial', 'cohort, retrospective',\n", + " 'cohort, prospective', 'cohort, clinical trial', 'GWAS',\n", + " 'case/control, GWAS', 'case/control, meta-analysis',\n", + " 'cohort, GWAS', 'meta-analysis, retrospective', 'prospective',\n", + " 'case series, retrospective',\n", + " 'case/control, clinical trial, retrospective',\n", + " 'cohort, clinical trial, prospective', 'cohort, replication',\n", + " 'case/control, retrospective', 'clinical trial, GWAS',\n", + " 'case/control, prospective', 'cohort, prospective, retrospective',\n", + " 'replication', 'cohort, case/control',\n", + " 'clinical trial, prospective', 'prospective, retrospective',\n", + " 'GWAS, replication', 'meta-analysis, GWAS',\n", + " 'case/control, clinical trial, prospective',\n", + " 'case series, prospective', 'cohort, meta-analysis',\n", + " 'case/control, GWAS, retrospective', 'cross sectional',\n", + " 'cohort, case series', 'case/control, case series',\n", + " 'clinical trial, meta-analysis', 'clinical trial, replication',\n", + " 'cohort, trios', 'replication, retrospective', 'GWAS, prospective',\n", + " 'cohort, clinical trial, retrospective',\n", + " 'clinical trial, GWAS, prospective',\n", + " 'case/control, clinical trial, prospective, retrospective',\n", + " 'case/control, meta-analysis, GWAS', 'trios',\n", + " 'clinical trial, retrospective', 'case/control, replication',\n", + " 'meta-analysis, prospective', 'case/control, clinical trial',\n", + " 'case series, clinical trial',\n", + " 'cohort, meta-analysis, retrospective', 'case series, trios',\n", + " 'meta-analysis, replication, prospective',\n", + " 'case series, meta-analysis', 'cohort, linkage',\n", + " 'meta-analysis, replication', 'cross sectional, GWAS',\n", + " 'case series, linkage', 'linkage', 'retrospective, linkage, trios',\n", + " 'case series, retrospective, trios',\n", + " 'cohort, case series, retrospective', 'cohort, GWAS, prospective',\n", + " 'cohort, replication, prospective',\n", + " 'clinical trial, meta-analysis, retrospective',\n", + " 'cohort, replication, retrospective', 'cohort, GWAS, replication',\n", + " 'cohort, cross sectional, retrospective', 'cohort, linkage, trios',\n", + " 'cohort, clinical trial, meta-analysis',\n", + " 'clinical trial, GWAS, retrospective',\n", + " 'case series, linkage, trios', 'linkage, trios',\n", + " 'cohort, meta-analysis, replication',\n", + " 'case series, retrospective, linkage',\n", + " 'cohort, case/control, GWAS', 'cohort, case/control, replication',\n", + " 'clinical trial, replication, prospective',\n", + " 'cohort, case/control, retrospective',\n", + " 'case/control, replication, retrospective',\n", + " 'cohort, clinical trial, GWAS', 'cohort, cross sectional',\n", + " 'case/control, case series, retrospective',\n", + " 'case/control, clinical trial, meta-analysis',\n", + " 'cohort, meta-analysis, prospective',\n", + " 'case/control, clinical trial, GWAS',\n", + " 'clinical trial, meta-analysis, GWAS',\n", + " 'clinical trial, meta-analysis, prospective',\n", + " 'cohort, GWAS, retrospective', 'GWAS, retrospective',\n", + " 'cohort, case series, prospective', 'cohort, meta-analysis, GWAS',\n", + " 'replication, prospective', 'meta-analysis, GWAS, retrospective',\n", + " 'clinical trial, meta-analysis, prospective, trios',\n", + " 'cross sectional, retrospective',\n", + " 'cohort, case/control, prospective',\n", + " 'GWAS, replication, retrospective',\n", + " 'clinical trial, meta-analysis, replication',\n", + " 'meta-analysis, GWAS, replication'], dtype=object)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "study_parameters[\"Study Type\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28525" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert var_drug_ann to pmid, Variant Annotation ID, and Variant Annotation Type\n", + "pmid_table = var_drug_ann[['PMID', 'Variant Annotation ID']]\n", + "pmid_table = pd.concat([pmid_table, var_fa_ann[['PMID', 'Variant Annotation ID']]])\n", + "pmid_table = pd.concat([pmid_table, var_pheno_ann[['PMID', 'Variant Annotation ID']]])\n", + "pmid_table.drop_duplicates(inplace=True)\n", + "len(pmid_table)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34836" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# join study parameters with pmid_table on Variant Annotation ID\n", + "study_parameters = study_parameters.merge(pmid_table, on='Variant Annotation ID', how='left')\n", + "len(study_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_params = study_parameters.sort_values(by=\"Variant Annotation ID\", inplace=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "base_json = {\n", + " \"pmid\": \"\",\n", + " \"var_drug_ann\": [],\n", + " \"var_fa_ann\": [],\n", + " \"var_pheno_ann\": []\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-09 11:57:58.637\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m6\u001b[0m - \u001b[1mLoaded 34836 pmid annotations from pickle\u001b[0m\n" + ] + } + ], + "source": [ + "# check for annotations_by_pmid.pkl\n", + "pmid_table = []\n", + "if os.path.exists(\"data/variantAnnotations/annotations_by_pmid.pkl\"):\n", + " with open(\"data/variantAnnotations/annotations_by_pmid.pkl\", \"rb\") as f:\n", + " pmid_table = pickle.load(f)\n", + " logger.info(f\"Loaded {len(pmid_table)} pmid annotations from pickle\")\n", + "else:\n", + " for index, row in tqdm(study_parameters.iterrows(), total=len(study_parameters)):\n", + " pmid = row['PMID']\n", + "\n", + " # Filter by Variant Annotation ID\n", + " var_drug_ann_filtered = var_drug_ann[var_drug_ann['PMID'] == pmid].to_dict(orient='records')\n", + " # Convert to list\n", + " var_fa_ann_filtered = var_fa_ann[var_fa_ann['PMID'] == pmid].to_dict(orient='records')\n", + "\n", + " var_pheno_ann_filtered = var_pheno_ann[var_pheno_ann['PMID'] == pmid].to_dict(orient='records')\n", + "\n", + " # Filter by Variant Annotation Type\n", + " pmid_addition = {\n", + " \"pmid\": pmid,\n", + " \"var_drug_ann\": var_drug_ann_filtered,\n", + " \"var_fa_ann\": var_fa_ann_filtered,\n", + " \"var_pheno_ann\": var_pheno_ann_filtered\n", + " }\n", + " pmid_table.append(pmid_addition)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# save pmid_table to jsonl\n", + "# import json\n", + "# with open(\"data/annotations_by_pmid.jsonl\", \"w\") as f:\n", + "# for pmid_addition in pmid_table:\n", + "# f.write(json.dumps(pmid_addition) + \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "pmcid_mapping = json.load(open(\"data/pmcid_mapping_updated.json\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "def get_title_from_markdown(markdown_path: str):\n", + " with open(markdown_path, \"r\") as f:\n", + " markdown_text = f.read()\n", + " # get the title from the markdown text\n", + " title = markdown_text.split(\"\\n\")[0]\n", + " # remove the # from the title\n", + " title = title.replace(\"# \", \"\")\n", + " return title\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/34836 [00:00 Date: Wed, 11 Jun 2025 15:19:34 -0700 Subject: [PATCH 05/28] feat: simple inference --- src/annotation_extraction/simple_inference.py | 79 +++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 src/annotation_extraction/simple_inference.py diff --git a/src/annotation_extraction/simple_inference.py b/src/annotation_extraction/simple_inference.py new file mode 100644 index 0000000..4013ef2 --- /dev/null +++ b/src/annotation_extraction/simple_inference.py @@ -0,0 +1,79 @@ +from typing import Dict +from loguru import logger +import litellm +from .inference import LLMInterface +import re +import os +from dotenv import load_dotenv +load_dotenv() + +class PromptGenerator: + def __init__(self, prompt_template: str, replacements: Dict[str, str]): + self.prompt_template = prompt_template + self.replacements = replacements + self.validate_replacements() + self.prompt = self.generate_prompt() + + def validate_replacements(self) -> bool: + """Validate that all replacement keys are present in the prompt template.""" + all_valid = True + for key in self.replacements: + if key not in self.prompt_template: + logger.warning(f"Replacement key {key} not found in prompt template.") + all_valid = False + return all_valid + + def generate_prompt(self) -> str: + return self.prompt_template.format(**self.replacements) + + def remove_references(self) -> str: + """Remove references from the prompt.""" + return re.sub(r'\[[0-9]+\]', '', self.prompt) + + def remove_references_section(self) -> str: + """ + Removes the references section from article text. + + Returns: + str: Article text with references section removed + (Looks for ## References section and removes it and everything after) + """ + # Split the text into sections + sections = self.prompt.split("##") + + # Find the index of the References section + ref_index = -1 + for i, section in enumerate(sections): + if section.strip().startswith("References"): + ref_index = i + break + + # If references section found, remove it and everything after + if ref_index != -1: + sections = sections[:ref_index] + return "##".join(sections) + + return self.prompt + + + def get_prompt(self) -> str: + return self.prompt + + + +class SimpleLLM(LLMInterface): + """Simple LLM interface that just returns the prompt.""" + debug_mode = False + + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): + + self.model = model + self.temperature = temperature + + if self.debug_mode: + litellm.set_verbose = True + + def generate(self, prompt: str, temperature: float = None) -> str: + temp = temperature if temperature is not None else self.temperature + response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], temperature=temp) + return response.choices[0].message.content \ No newline at end of file From 5553d28e892b00494f90455657df12b880ea6376 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Wed, 11 Jun 2025 22:05:57 -0700 Subject: [PATCH 06/28] checkpoint: testing prompts for extracting article info --- .gitignore | 3 +- data/pmcid_mapping_updated.json | 8367 +++++++++++++++++ docs/annotation_prompts.md | 665 ++ docs/llm_annotation_method.md | 378 + notebooks/create_pmid_annotations.ipynb | 283 +- notebooks/exploration.ipynb | 3209 +++++++ notebooks/test.ipynb | 675 ++ notebooks/update_pmcid_mapping.ipynb | 22 - pixi.lock | 326 +- pixi.toml | 1 + src/annotation_extraction/__init__.py | 38 + src/annotation_extraction/example_usage.py | 274 + src/annotation_extraction/inference.py | 422 + src/annotation_extraction/models.py | 224 + src/annotation_extraction/pipeline.py | 421 + src/annotation_extraction/prompts.py | 291 + src/annotation_extraction/simple_inference.py | 30 - src/annotation_extraction/simple_prompts.py | 9 + src/annotation_extraction/testing.ipynb | 0 19 files changed, 15412 insertions(+), 226 deletions(-) create mode 100644 data/pmcid_mapping_updated.json create mode 100644 docs/annotation_prompts.md create mode 100644 docs/llm_annotation_method.md create mode 100644 notebooks/exploration.ipynb create mode 100644 notebooks/test.ipynb delete mode 100644 notebooks/update_pmcid_mapping.ipynb create mode 100644 src/annotation_extraction/__init__.py create mode 100644 src/annotation_extraction/example_usage.py create mode 100644 src/annotation_extraction/inference.py create mode 100644 src/annotation_extraction/models.py create mode 100644 src/annotation_extraction/pipeline.py create mode 100644 src/annotation_extraction/prompts.py create mode 100644 src/annotation_extraction/simple_prompts.py create mode 100644 src/annotation_extraction/testing.ipynb diff --git a/.gitignore b/.gitignore index aeaf421..a595a0d 100644 --- a/.gitignore +++ b/.gitignore @@ -38,4 +38,5 @@ data/markdown *.log -benchmark_results/ \ No newline at end of file +benchmark_results/ +test_outputs/ \ No newline at end of file diff --git a/data/pmcid_mapping_updated.json b/data/pmcid_mapping_updated.json new file mode 100644 index 0000000..29ac2c7 --- /dev/null +++ b/data/pmcid_mapping_updated.json @@ -0,0 +1,8367 @@ +{ + "3335642": "PMC442471", + "21528942": null, + "24589909": null, + "23602689": null, + "22571356": null, + "15475735": null, + "24033383": null, + "19545678": null, + "29589488": null, + "25096692": null, + "18987660": null, + "28689179": "PMC5749368", + "21498394": null, + "36246675": "PMC9532634", + "36988399": null, + "23623526": "PMC7274090", + "17324110": null, + "25285015": "PMC4181635", + "24898899": null, + "21930396": "PMC3640375", + 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"PMC2955872", + "28403410": "PMC6906626", + "24879639": "PMC4168829", + "38216550": "PMC10786722", + "24107927": "PMC4112558", + "15201141": null, + "32149563": null, + "10739168": null, + "16842817": null, + "26863581": null, + "16609362": null, + "11505220": null, + "15797993": null, + "33446525": null, + "25447891": null, + "19022938": "PMC2680526", + "23328581": "PMC3602211", + "25287681": "PMC4411934", + "26488616": "PMC4674354", + "30822619": "PMC6498441", + "29344738": null, + "21593735": null, + "23651026": null, + "17885621": null, + "16847428": null, + "11037806": null +} \ No newline at end of file diff --git a/docs/annotation_prompts.md b/docs/annotation_prompts.md new file mode 100644 index 0000000..d19de43 --- /dev/null +++ b/docs/annotation_prompts.md @@ -0,0 +1,665 @@ +# LLM-Based Article Annotation Method for Pharmacogenomic Variant Annotations + +## Overview + +This document outlines a comprehensive approach for using Large Language Models (LLMs) to extract structured pharmacogenomic variant annotations from biomedical articles. The method is designed to populate the AutoGKB variant annotation database with information about drug-gene interactions, variant effects, and clinical outcomes. + +## Input and Output Structure + +### Input +- **Article**: Markdown-formatted biomedical articles from PubMed Central (PMC) +- **Format**: Each article contains metadata (title, authors, PMID, PMCID, DOI) and full text content + +### Output +Three types of structured annotations corresponding to TSV files: + +1. **Functional Annotations (var_fa_ann.tsv)** - In vitro/molecular level effects +2. **Drug Annotations (var_drug_ann.tsv)** - Clinical drug response associations +3. **Phenotype Annotations (var_pheno_ann.tsv)** - Disease/adverse event associations + +Each annotation contains 20+ standardized fields including variant information, genes, drugs, phenotypes, population details, and comparison groups. + +## Multi-Stage LLM Pipeline +### Stage 1: Annotation Determination +**Objective**: Determine what annotations make the most sense to include from the article +**Prompt Template**: +``` +You are determining what type of annotations are appropritate for the article from the options of to extract from a biomedical article. For the article determine the following: + +Article: \n\n{article_text}\n\n + +Articles get Variant Drug Annotations when they report associations between genetic variants and +pharmacological parameters or clinical drug response measures that specifically relate to: +Drug Annotations (var_drug_ann.tsv) include: + +- Pharmacokinetic/Pharmacodynamic Parameters +- Clinical phenotypes/adverse events (Drug toxicity, organ dysfunction, treatment response phenotypes, disease outcomes when treated with drugs) + +Articles get Variant Phenotype Annotations when they report associations between genetic variants and adverse drug reactions, toxicities, or clinical outcomes that represent: +- Toxicity/Safety outcomes +- Clinical phenotypes/adverse events + +Articles get Variant Functional Annotations when they contain in vitro or mechanistic functional studies that directly measure how genetic variants affect: + +- Enzyme/transporter activity (e.g., clearance, metabolism, transport) +- Binding affinity (e.g., protein-drug interactions) +- Functional properties (e.g., uptake rates, kinetic parameters like Km/Vmax) + +The key distinction is mechanistic functional studies (gets var_fa_ann) vs clinical association studies (gets var_drug_ann or var_pheno_ann but not var_fa_ann). + +Examples: + +- "Cardiotoxicity when treated with anthracyclines" → var_pheno_ann +- "Decreased clearance of methotrexate" → var_drug_ann +- "Decreased enzyme activity in cell culture" → var_fa_ann +- "Variant affects drug clearance/response" —> var_drug_ann +- "Variant affects adverse events/toxicity outcomes" —> var_pheno_ann +- "Variant affects protein function in laboratory studies" —> var_fa_ann + +Using this information, decide which out of the 3 annotations the article should receive with a one sentence summary reason along with a sentence/quote from the article that indicates why this is true. + +Output Format: +Variant Drug Annotation: (Y/N) +Reason: (Reason) +Quote:(Quote) + +Variant Phenotype Annotation: (Y/N) +Reason: (Reason) +Quote:(Quote) + +Variant Functional Annotation: (Y/N) +Reason: (Reason) +Quote:(Quote) +``` + +TODO: Scrape this and get a structured output or create a structured ouput from this directly + + +### Stage 2A: Var Drug Tailored Article Summary + +**Objective**: Summarize all the key information from an article necessary for a var_drug_ann entry + +**Prompt Template**: +``` +You are extracting pharmacogenomic information from a biomedical article. Identify and extract the following entities and their relationships: + +Article: {article_text} + +Extract: +METHODS SECTION: +- Briefly summarize the methods and results of the study + +GENETIC VARIANTS (if present): +- SNP IDs (rs numbers) + - For each SNP, capture: + - Specific gene location + - Variant type (missense, synonymous, etc.) + - Potential functional impact on protein + - Observed genotype frequencies + - Explicitly link each genetic variant to: + - Specific drug response + - Changes in clinical outcomes + - Mechanism of pharmacological interaction +- Gene names and symbols +- Variant/haplotype names (e.g., CYP2D6*4, *1/*2) +- Allele designations +- Genotype combinations +- If/how the variants affected the outcome of the study + +DRUGS AND INTERVENTIONS: +- Drug names (generic and brand) +- Drug classes and categories +- Dosing information +- Treatment regimens + +PHENOTYPES AND OUTCOMES: +- Clinical phenotypes +- Adverse events and toxicities +- Efficacy measures +- Pharmacokinetic parameters (clearance, metabolism, etc.) +- Disease conditions +- If/how the phenotypes affected the outcome of the study + +POPULATION INFORMATION: +- Study population characteristics +- Demographics (age, ethnicity, gender) +- Sample sizes +- Inclusion/exclusion criteria + +ASSOCIATIONS AND RELATIONSHIPS: +- Statistical associations between variants and outcomes +- Odds ratios, p-values, confidence intervals +- Comparative statements (increased/decreased effects) +- Causal relationships + +Format your response as structured lists with clear entity types and associated context. + +``` +Notes: +- The methods section right now is pretty good, it captures most of the information. +- Next is converting this to an extractable format + +### Stage 3A: Extracting Drug Terms +This takes the raw articles --> drug terms +I think raw articles makes sense because we need quotes +``` +You are an expert pharmacogenomics researcher reading and extracting annotations from the following article + +\n\n{article_text}\n\n + +These are the following terms for which we need to extract values: + +Term: Variant/Haplotypes +- Content: The specific genetic variant mentioned in the study +- Manual Process: Look for SNP IDs (rs numbers), star alleles (CYP2D6*4), or genotype combinations +- Example: rs2909451, CYP2C19*1, CYP2C19*2, *1/*18 + +Term: Gene +- Content: Gene symbol associated with the variant +- Manual Process: Find the gene name near the variant mention, use standard HUGO symbols +- Example: DPP4, CYP2C19, KCNJ11 + +Term: Drug(s) +- Content: Generic drug name(s) studied +- Manual Process: Extract drug names from methods/results, use generic names, separate multiple drugs with commas +- Example: sitagliptin, clopidogrel, aspirin + +Term: Phenotype Category +- Content: Type of clinical outcome studied +- Manual Process: Categorize based on what was measured: + - Efficacy: Treatment response, clinical improvement + - Metabolism/PK: Drug levels, clearance, half-life + - Toxicity: Adverse events, side effects + - Dosage: Dose requirements, dose adjustments + - Other: Everything else +- Example: Efficacy (for HbA1c improvement study) + +Term: Significance +- Content: Whether the association was statistically significant +- Manual Process: Look for p-values, confidence intervals: + - yes: p < 0.05 or explicitly stated as significant + - no: p ≥ 0.05 or stated as non-significant + - not stated: No statistical testing mentioned +- Example: yes (P < .001 in sitagliptin study) + +Term: Notes +- Content: Key study details, methodology, or important context +- Manual Process: Extract relevant quotes showing statistical results, study design, or important caveats +- Example: "Patients with the rs2909451 TT genotype in the study group exhibited a median HbA1c improvement of 0.57..." + +Term: Standardized Sentence + +- Content: Standardized description of the genetic association +- Manual Process: Write in format: "[Genotype/Allele] is [associated with/not associated with] [increased/decreased] +[outcome] [drug context] [population context]" +- Example: "Genotype TT is associated with decreased response to sitagliptin in people with Diabetes Mellitus, Type 2." + +Term: Alleles + +- Content: Specific allele or genotype if different from Variant/Haplotypes field +- Manual Process: Extract the exact genotype mentioned (AA, TT, CC, del/del, etc.) +- Example: TT, *1/*18, del/del + +Term: Metabolizer types + +- Content: CYP enzyme phenotype categories +- Manual Process: Look for metabolizer classifications in CYP studies: + - poor metabolizer, intermediate metabolizer, extensive metabolizer, ultrarapid metabolizer +- Example: intermediate metabolizer + +Term: Comparison Allele(s) or Genotype(s) + +- Content: Reference genotype used for comparison +- Manual Process: Find what the study variant was compared against +- Example: *1/*1, C (for wild-type comparisons) + +Term: Comparison Metabolizer types + +- Content: Reference metabolizer status for comparison +- Manual Process: Extract the comparison metabolizer phenotype +- Example: normal metabolizer + +Term: Specialty Population + +- Content: Age-specific populations +- Manual Process: Check if study specifically focused on: + - Pediatric: Children/adolescents + - Geriatric: Elderly patients + - Leave empty for general adult populations + +Term: Population types +- Content: Descriptor of study population +- Manual Process: Look for population descriptors, usually "in people with" or ethnicity information +- Example: in people with + +Term: Population Phenotypes or diseases +- Content: Disease/condition context with standardized prefix +- Manual Process: Find the medical condition studied, add appropriate prefix: + - Disease: for established diseases + - Other: for conditions/traits + - Side Effect: for adverse events +- Example: Other:Diabetes Mellitus, Type 2 + +Term: isPlural +- Content: Grammar helper for sentence construction +- Manual Process: Use Is for singular subjects, Are for plural +- Example: Is + +Term: Is/Is Not associated +- Content: Direction of association +- Manual Process: Determine if association was: + - Associated with: Positive association found + - Not associated with: No association found +- Example: Associated with + +Term: Direction of effect + +- Content: Whether the effect increases or decreases the outcome +- Manual Process: Look for directional language: + - increased: Higher levels, better response, more effect + - decreased: Lower levels, worse response, less effect + - Leave empty if no clear direction +- Example: decreased + +Term: PD/PK terms + +- Content: Pharmacological outcome descriptor +- Manual Process: Extract the specific outcome measured: + - response to, concentrations of, metabolism of, clearance of, dose of +- Example: response to + +Term: Multiple drugs And/or + +- Content: Logical connector for multiple drugs +- Manual Process: If multiple drugs mentioned: + - and: All drugs together + - or: Any of the drugs + - Leave empty for single drug + +Term: Multiple phenotypes or diseases And/or + +- Content: Logical connector for multiple conditions +- Manual Process: Similar to drugs, use and/or for multiple conditions +- Leave empty for single condition + +General recommended strategies + +1. Scan for genetic variants: Look for "rs" numbers, gene names with asterisks, or phrases like "genotype," "allele," +"polymorphism" +2. Identify drug context: Find drug names in methods, results, or discussion sections +3. Locate outcome measures: Look for clinical endpoints, lab values, response rates, adverse events +4. Find statistical associations: Search for p-values, odds ratios, significant differences between genotype groups +5. Extract population details: Note the study population, disease context, and inclusion criteria +6. Standardize the relationship: Convert the finding into the standardized sentence format following the association pattern + +For each term, the output should be of the format: + +Extracted Output: (output) +Reason: (one sentence justification) +Quote: (quote from the article that demonstrates why) +``` + +### Stage 3B: Extracting Pheno Terms +``` +Phenotype Association Annotation Guidelines + +Article: \n\n{article_text}\n\n + + +## Terms for Extraction + +### Variant/Haplotypes +- **Content**: The specific genetic variant studied +- **Manual Process**: Extract SNP IDs (rs numbers), HLA alleles, star alleles, or genotype combinations +- **Example**: HLA-B*35:08, rs1801272, UGT1A1*1, UGT1A1*28 + +### Gene +- **Content**: Gene symbol associated with the variant +- **Manual Process**: Find the gene name near the variant mention +- **Example**: HLA-B, CYP2A6, UGT1A1 + +### Drug(s) +- **Content**: Drug(s) that caused or were involved in the phenotype +- **Manual Process**: + - Extract drug names that triggered the adverse event or phenotype + - Leave empty for disease susceptibility studies without drug involvement +- **Example**: lamotrigine, sacituzumab govitecan, empty for disease predisposition + +### Phenotype Category +- **Content**: Type of phenotype or outcome studied +- **Manual Process**: Categorize based on primary outcome: + - Toxicity: Adverse drug reactions, side effects, drug-induced toxicity + - Efficacy: Treatment response, therapeutic outcomes + - Metabolism/PK: Pharmacokinetic parameters, drug levels + - Dosage: Dose requirements, dose-response relationships + - Other: Disease susceptibility, traits not directly drug-related +- **Example**: + - Toxicity (for Stevens-Johnson Syndrome) + - Other (for alcoholism risk) + +### Significance +- **Content**: Statistical significance of the association +- **Manual Process**: Look for p-values and statistical tests: + - yes: p < 0.05 or stated as significant + - no: p ≥ 0.05 or explicitly non-significant + - not stated: No statistical testing reported +- **Example**: no (for non-significant HLA associations) + +### Notes +- **Content**: Key study details, statistics, methodology +- **Manual Process**: Extract relevant quotes showing statistical results, case descriptions, or important context +- **Example**: "The allele was not significant when comparing allele frequency in cases..." + +### Standardized Sentence +- **Content**: Standardized description of the genetic-phenotype association +- **Manual Process**: Write in format: "[Variant] is [associated with/not associated with] [increased/decreased] [phenotype outcome] [drug context] [population context]" +- **Example**: "HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy." + +### Alleles +- **Content**: Specific allele or genotype if different from main variant field +- **Manual Process**: Extract the exact genotype mentioned +- **Example**: *35:08, AA + AT, *1/*28 + *28/*28 + +### Specialty Population +- **Content**: Age-specific populations +- **Manual Process**: Identify if study focused on specific age groups: + - Pediatric: Children/adolescents + - Geriatric: Elderly patients + - Leave empty for general adult populations +- **Example**: Pediatric (for children with Fanconi Anemia) + +### Metabolizer Types +- **Content**: CYP enzyme phenotype when applicable +- **Manual Process**: Look for metabolizer classifications in CYP studies: + - poor metabolizer + - intermediate metabolizer + - extensive metabolizer + - ultrarapid metabolizer + - deficiency +- **Example**: ultrarapid metabolizer, intermediate activity + +### isPlural +- **Content**: Grammar helper for sentence construction +- **Manual Process**: Use Is for singular subjects, Are for plural +- **Example**: Is (for single allele), Are (for combined genotypes) + +### Is/Is Not Associated +- **Content**: Direction of statistical association +- **Manual Process**: Determine association type: + - Associated with: Positive association found + - Not associated with: No association found +- **Example**: Not associated with, Associated with + +### Direction of Effect +- **Content**: Whether the variant increases or decreases the phenotype +- **Manual Process**: Look for directional language: + - increased: Higher risk, more severe, greater likelihood + - decreased: Lower risk, less severe, reduced likelihood + - Leave empty if no clear direction +- **Example**: + - increased (for higher toxicity risk) + - decreased (for lower disease risk) + +### Side Effect/Efficacy/Other +- **Content**: Specific phenotype outcome with standardized prefix +- **Manual Process**: Categorize the phenotype and add appropriate prefix: + - Side Effect: for adverse drug reactions + - Efficacy: for therapeutic outcomes + - Disease: for disease conditions + - Other: for other traits/conditions + - PK: for pharmacokinetic measures +- **Example**: + - Side Effect:Stevens-Johnson Syndrome + - Disease:Alcohol abuse + - Other:Medication adherence + +### When Treated With/Exposed To/When Assayed With +- **Content**: Drug administration context +- **Manual Process**: Use standard phrases: + - when treated with: For therapeutic drug administration + - when exposed to: For environmental or non-therapeutic exposure + - due to: For substance-related disorders + - Leave empty for non-drug phenotypes +- **Example**: when treated with, due to (for substance abuse) + +### Multiple Drugs And/Or +- **Content**: Logical connector for multiple drugs +- **Manual Process**: If multiple drugs involved: + - and: Combination therapy + - or: Any of the drugs + - Leave empty for single drug +- **Example**: or (for any of several drugs) + +### Population Types +- **Content**: Description of study population +- **Manual Process**: Look for population descriptors: + - in people with: General population with condition + - in children with: Pediatric population + - in women with: Gender-specific population +- **Example**: in people with, in children with + +### Population Phenotypes or Diseases +- **Content**: Disease/condition context with prefix +- **Manual Process**: Find the medical condition and add prefix: + - Disease: for established diseases + - Other: for conditions/traits +- **Example**: + - Disease:Epilepsy + - Other:Diabetes Mellitus, Type 2 + +### Multiple Phenotypes or Diseases And/Or +- **Content**: Logical connector for multiple conditions +- **Manual Process**: Use and/or for multiple disease contexts +- **Example**: and (for multiple comorbidities) + +### Comparison Allele(s) or Genotype(s) +- **Content**: Reference genotype for comparison +- **Manual Process**: Find what the variant was compared against +- **Example**: TT (wild-type), *1/*1 (normal function allele) + +### Comparison Metabolizer Types +- **Content**: Reference metabolizer phenotype +- **Manual Process**: Extract comparison metabolizer status +- **Example**: normal metabolizer + +## General Strategy Recommendations + +1. **Identify Phenotype Outcomes**: Look for adverse events, toxicities, disease conditions, clinical traits +2. **Find Genetic Associations**: Search for variants linked to the phenotype (may or may not involve drugs) +3. **Determine Drug Involvement**: Check if phenotype is drug-induced or related to disease susceptibility +4. **Extract Statistical Evidence**: Look for odds ratios, p-values, case reports, frequency differences +5. **Categorize Phenotype Type**: Classify as toxicity, efficacy, disease susceptibility, or other trait +6. **Note Population Context**: Identify specific patient populations, age groups, disease conditions +7. **Standardize the Relationship**: Convert findings into standardized sentence format describing the genetic-phenotype association + +For each term, the output should be of the format: + +Extracted Output: (output) +Reason: (one sentence justification) +Quote: (quote from the article that demonstrates why) +``` + +### Stage 3C: Extracting FA Terms +``` +# Functional Annotation Guidelines + +## Terms for Extraction + +### Variant/Haplotypes +- **Content**: The specific genetic variant studied +- **Manual Process**: Extract variant names, star alleles, SNP IDs, or protein constructs tested +- **Example**: CYP2C19*1, CYP2C19*17, rs72552763, CYP2B6*1, CYP2B6*6 + +### Gene +- **Content**: Gene symbol associated with the variant +- **Manual Process**: Identify the gene being studied functionally +- **Example**: CYP2C19, CYP2B6, SLC22A1 + +### Drug(s) +- **Content**: Substrate or compound used in the functional assay +- **Manual Process**: Extract the drug/substrate used to test enzyme activity or transport +- **Example**: normeperidine, bupropion, warfarin, voriconazole + +### Phenotype Category +- **Content**: Type of functional outcome measured +- **Manual Process**: Categorize based on what was measured: + - Metabolism/PK: Enzyme activity, clearance, transport, binding affinity + - Efficacy: Functional response in cellular systems + - Leave empty for basic biochemical studies +- **Example**: + - Metabolism/PK (for enzyme kinetics) + - Efficacy (for cellular response) + +### Significance +- **Content**: Statistical significance of functional differences +- **Manual Process**: Look for statistical comparisons: + - yes: Significant differences in activity/function + - no: No significant differences + - not stated: No statistical testing reported +- **Example**: + - yes (for significant activity differences) + - not stated (for descriptive studies) + +### Notes +- **Content**: Key experimental details, methodology, quantitative results +- **Manual Process**: Extract relevant quotes showing experimental conditions, numerical results, or important technical details +- **Example**: "Clearance was 26.57% of wild-type. CYP2C19 variants expressed in Sf21 insect cells..." + +### Standardized Sentence +- **Content**: Standardized description of the functional relationship +- **Manual Process**: Write in format: "[Variant] is associated with [increased/decreased] [functional outcome] [experimental context] as compared to [reference variant]" +- **Example**: "CYP2C19 *17/*17 is associated with increased formation of normeperidine as compared to CYP2C19 *1/*1 + *1/*17." + +### Alleles +- **Content**: Specific allele or genotype tested +- **Manual Process**: Extract the exact variant designation +- **Example**: *17/*17, *1/*1, del, A + +### Metabolizer Types +- **Content**: Phenotype classification if applicable +- **Manual Process**: Rarely used in functional studies; mainly for CYP phenotyping +- **Example**: Usually empty + +### Comparison Allele(s) or Genotype(s) +- **Content**: Reference variant for comparison +- **Manual Process**: Find the control/wild-type variant used for comparison +- **Example**: *1/*1 + *1/*17, *1, GAT + +### Comparison Metabolizer Types +- **Content**: Reference metabolizer status +- **Manual Process**: Usually empty for functional studies +- **Example**: Usually empty + +### Assay Type +- **Content**: Laboratory method or experimental system used +- **Manual Process**: Extract the specific assay methodology: + - in human liver microsomes: Microsomal enzyme assays + - hydroxylation assay: Specific metabolic pathway assays + - crystal structure prediction: Computational modeling + - Leave empty if not specified +- **Example**: + - in human liver microsomes + - hydroxylation assay + - crystal structure prediction + +### Cell Type +- **Content**: Cell line or tissue system used for the assay +- **Manual Process**: Extract the specific cellular context: + - 293FT cells: Human embryonic kidney cells + - COS-7 cells: Monkey kidney cells + - Sf21 insect cells: Insect cells for baculovirus expression + - in insect microsomes: Microsomal preparations + - expressed in [cell type]: Heterologous expression systems +- **Example**: + - in 293FT cells + - expressed in COS-7 cells + +### Specialty Population +- **Content**: Age-specific populations (rarely applicable to functional studies) +- **Manual Process**: Usually leave empty for in vitro studies +- **Example**: Usually empty + +### isPlural +- **Content**: Grammar helper for sentence construction +- **Manual Process**: Use Is for singular subjects, Are for plural +- **Example**: Is + +### Is/Is Not Associated +- **Content**: Direction of functional association +- **Manual Process**: Determine association type: + - Associated with: Functional difference observed + - Not associated with: No functional difference +- **Example**: Associated with + +### Direction of Effect +- **Content**: Whether the variant increases or decreases function +- **Manual Process**: Look for directional language: + - increased: Higher activity, better function, enhanced capability + - decreased: Lower activity, reduced function, impaired capability +- **Example**: + - increased (for enhanced activity) + - decreased (for reduced activity) + +### Functional Terms +- **Content**: Specific functional outcome measured +- **Manual Process**: Extract the precise functional parameter: + - activity of: Enzyme activity measurements + - clearance of: Drug clearance kinetics + - formation of: Metabolite formation + - transport of: Transporter function + - affinity to: Binding affinity + - catalytic activity of: Catalytic efficiency +- **Example**: + - formation of + - activity of + - clearance of + +### Gene/Gene Product +- **Content**: Specific gene or protein being functionally assessed +- **Manual Process**: Extract the gene symbol when the functional term relates to gene product activity +- **Example**: CYP2C19, CYP2B6, CYP2C9 + +### When Treated With/Exposed To/When Assayed With +- **Content**: Experimental substrate context +- **Manual Process**: Use standard phrases for functional assays: + - when assayed with: For enzyme activity assays + - of: For direct metabolite measurements + - Leave empty for non-substrate specific functions +- **Example**: + - when assayed with + - of + +### Multiple Drugs And/Or +- **Content**: Logical connector for multiple substrates +- **Manual Process**: If multiple substrates tested: + - and: Combination substrate assays + - or: Alternative substrate assays + - Leave empty for single substrate +- **Example**: or (for alternative substrates) + +## Manual Reading Strategy for Functional Annotations + +1. **Identify Experimental System**: Look for cell lines, microsomes, expression systems, computational models +2. **Find Functional Readouts**: Search for enzyme activity, kinetic parameters, binding affinity, transport rates +3. **Extract Substrate Information**: Identify the drug/compound used to test function +4. **Locate Comparison Data**: Find reference variants (usually wild-type or *1 alleles) for comparison +5. **Quantify Functional Changes**: Look for fold-changes, percentages, kinetic parameters (Km, Vmax, clearance) +6. **Note Experimental Conditions**: Extract assay conditions, expression systems, substrate concentrations +7. **Standardize the Relationship**: Convert findings into standardized sentence format describing the functional difference + +## Key Differences from Clinical Annotations + +- **Laboratory-based**: In vitro studies rather than patient studies +- **Mechanistic Focus**: How variants affect protein function rather than clinical outcomes +- **Quantitative Measures**: Enzyme kinetics, binding constants, activity percentages +- **Controlled Conditions**: Defined experimental systems rather than clinical populations +- **Substrate-specific**: Effects measured with specific drugs/compounds as substrates + +**Purpose**: Functional annotations provide the mechanistic basis for understanding why certain variants affect drug response in patients - they show how genetic changes alter protein function at the molecular level. + +For each term, the output should be of the format: + +Extracted Output: (output) +Reason: (one sentence justification) +Quote: (sentence from the article that demonstrates why) +``` \ No newline at end of file diff --git a/docs/llm_annotation_method.md b/docs/llm_annotation_method.md new file mode 100644 index 0000000..59fa6cb --- /dev/null +++ b/docs/llm_annotation_method.md @@ -0,0 +1,378 @@ +# LLM-Based Article Annotation Method for Pharmacogenomic Variant Annotations + +## Overview + +This document outlines a comprehensive approach for using Large Language Models (LLMs) to extract structured pharmacogenomic variant annotations from biomedical articles. The method is designed to populate the AutoGKB variant annotation database with information about drug-gene interactions, variant effects, and clinical outcomes. + +## Input and Output Structure + +### Input +- **Article**: Markdown-formatted biomedical articles from PubMed Central (PMC) +- **Format**: Each article contains metadata (title, authors, PMID, PMCID, DOI) and full text content + +### Output +Three types of structured annotations corresponding to TSV files: + +1. **Functional Annotations (var_fa_ann.tsv)** - In vitro/molecular level effects +2. **Drug Annotations (var_drug_ann.tsv)** - Clinical drug response associations +3. **Phenotype Annotations (var_pheno_ann.tsv)** - Disease/adverse event associations + +Each annotation contains 20+ standardized fields including variant information, genes, drugs, phenotypes, population details, and comparison groups. + +## Multi-Stage LLM Pipeline + +### Stage 1: Article Relevance Screening + +**Objective**: Determine if the article contains pharmacogenomic variant information worthy of annotation. + +**Prompt Template**: +``` +You are a pharmacogenomics expert reviewing biomedical literature. Analyze this article and determine if it contains information about genetic variants and their associations with drug response, metabolism, toxicity, or disease phenotypes. + +Article Title: {title} +Article Text: {article_text} + +Does this article contain: +1. Specific genetic variants (SNPs, haplotypes, alleles)? +2. Drug names or therapeutic interventions? +3. Clinical outcomes, drug responses, or phenotypic associations? +4. Population or patient cohort data? + +Respond with: RELEVANT or NOT_RELEVANT +If RELEVANT, provide a brief 2-3 sentence summary of the key pharmacogenomic findings. +``` + +### Stage 2: Entity Extraction and Relationship Identification + +**Objective**: Extract all relevant pharmacogenomic entities and their relationships from the article. + +**Prompt Template**: +``` +You are extracting pharmacogenomic information from a biomedial article. Identify and extract the following entities and their relationships: + +Article: {article_text} + +Extract: + +GENETIC VARIANTS: +- SNP IDs (rs numbers) +- Gene names and symbols +- Variant/haplotype names (e.g., CYP2D6*4, *1/*2) +- Allele designations +- Genotype combinations + +DRUGS AND INTERVENTIONS: +- Drug names (generic and brand) +- Drug classes and categories +- Dosing information +- Treatment regimens + +PHENOTYPES AND OUTCOMES: +- Clinical phenotypes +- Adverse events and toxicities +- Efficacy measures +- Pharmacokinetic parameters (clearance, metabolism, etc.) +- Disease conditions + +POPULATION INFORMATION: +- Study population characteristics +- Demographics (age, ethnicity, gender) +- Sample sizes +- Inclusion/exclusion criteria + +ASSOCIATIONS AND RELATIONSHIPS: +- Statistical associations between variants and outcomes +- Odds ratios, p-values, confidence intervals +- Comparative statements (increased/decreased effects) +- Causal relationships + +Format your response as structured lists with clear entity types and associated context. +``` + +### Stage 3: Annotation Type Classification + +**Objective**: Determine which type(s) of annotations should be created for each variant-outcome relationship. + +**Prompt Template**: +``` +Based on the extracted entities and relationships, classify each variant-outcome association into annotation categories: + +Extracted Relationships: {relationships} + +Classification Rules: +- FUNCTIONAL ANNOTATION: In vitro studies, enzyme activity, protein expression, cellular assays, metabolism studies +- DRUG ANNOTATION: Clinical drug response, efficacy, dosing, therapeutic outcomes in patients +- PHENOTYPE ANNOTATION: Adverse events, toxicity, disease susceptibility, clinical phenotypes + +For each relationship, specify: +1. Annotation type(s) applicable +2. Confidence level (high/medium/low) +3. Key evidence supporting the classification + +Output format: +Relationship ID | Annotation Type(s) | Confidence | Evidence Summary +``` + +### Stage 4: Schema-Specific Row Generation + +**Objective**: Generate complete TSV row entries matching the exact database schema. + +#### 4.1 Functional Annotation Row Generation (var_fa_ann.tsv) + +**Prompt Template**: +``` +Generate a complete functional annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. + +Article PMID: {pmid} +Relationship: {relationship_description} +Relevant Text: {supporting_sentence} + +REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: + +Variant Annotation ID: [Generate unique ID: concatenate timestamp + random numbers] +Variant/Haplotypes: [Exact variant notation, e.g., "CYP2C19*1, CYP2C19*2" or "rs1234567"] +Gene: [Gene symbol only, e.g., "CYP2C19"] +Drug(s): [Generic drug name(s), e.g., "warfarin" or "clopidogrel, aspirin"] +PMID: [{pmid}] +Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] +Significance: [EXACTLY ONE: "yes", "no", "not stated"] +Notes: [Brief context or methodology, e.g., "in vitro expression study"] +Sentence: [Complete supporting sentence from article that describes the association] +Alleles: [Specific allele if different from Variant/Haplotypes, or empty] +Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or leave empty] +Assay type: [Laboratory method, e.g., "yeast microsomes", "human liver microsomes"] +Metabolizer types: [If applicable: "poor metabolizer", "intermediate metabolizer", etc.] +isPlural: [Leave empty - automatically determined] +Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] +Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty if no direction] +Functional terms: [Specific functional outcome, e.g., "activity of", "metabolism of", "clearance of"] +Gene/gene product: [Gene symbol if functional term relates to gene product] +When treated with/exposed to/when assayed with: [Drug context if applicable] +Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] +Cell type: [If in vitro study, specify cell type] +Comparison Allele(s) or Genotype(s): [Reference genotype for comparison] +Comparison Metabolizer types: [Reference metabolizer status if applicable] + +OUTPUT EXACTLY ONE TAB-SEPARATED ROW with all fields filled or empty as appropriate. +Example output format: +1451148445 CYP2C19*1, CYP2C19*17 CYP2C19 normeperidine 30902024 Metabolism/PK not stated In other in vitro experiments... CYP2C19 *17/*17 is associated with increased formation of normeperidine as compared to CYP2C19 *1/*1 + *1/*17. *17/*17 in human liver microsomes Is Associated with increased formation of *1/*1 + *1/*17 +``` + +#### 4.2 Drug Annotation Row Generation (var_drug_ann.tsv) + +**Prompt Template**: +``` +Generate a complete drug annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. + +Article PMID: {pmid} +Relationship: {relationship_description} +Relevant Text: {supporting_sentence} + +REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: + +Variant Annotation ID: [Generate unique ID] +Variant/Haplotypes: [Exact variant notation] +Gene: [Gene symbol only] +Drug(s): [Generic drug name(s)] +PMID: [{pmid}] +Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] +Significance: [EXACTLY ONE: "yes", "no", "not stated"] +Notes: [Clinical context or study details] +Sentence: [Complete supporting sentence from article] +Alleles: [Specific allele if different from Variant/Haplotypes, or empty] +Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or empty] +Metabolizer types: [If applicable: metabolizer status] +isPlural: [Leave empty] +Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] +Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty] +PD/PK terms: [Pharmacokinetic/pharmacodynamic outcome, e.g., "concentrations of", "response to"] +Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] +Population types: [Ethnicity/ancestry if specified] +Population Phenotypes or diseases: [Disease with prefix: "Disease:", "Other:", "Side Effect:"] +Multiple phenotypes or diseases And/or: [EXACTLY ONE: "and", "or", or empty] +Comparison Allele(s) or Genotype(s): [Reference genotype] +Comparison Metabolizer types: [Reference metabolizer status] + +OUTPUT EXACTLY ONE TAB-SEPARATED ROW. +Example output format: +1451834452 CYP3A4*1, CYP3A4*17 CYP3A4 nifedipine 15634941 Other, Metabolism/PK not stated in vitro expression... CYP3A4 *17 is associated with decreased metabolism of nifedipine as compared to CYP3A4 *1. *17 Is Associated with decreased metabolism of *1 +``` + +#### 4.3 Phenotype Annotation Row Generation (var_pheno_ann.tsv) + +**Prompt Template**: +``` +Generate a complete phenotype annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. + +Article PMID: {pmid} +Relationship: {relationship_description} +Relevant Text: {supporting_sentence} + +REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: + +Variant Annotation ID: [Generate unique ID] +Variant/Haplotypes: [Exact variant notation] +Gene: [Gene symbol only] +Drug(s): [Generic drug name(s)] +PMID: [{pmid}] +Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] +Significance: [EXACTLY ONE: "yes", "no", "not stated"] +Notes: [Clinical context] +Sentence: [Complete supporting sentence from article] +Alleles: [Specific allele if different, or empty] +Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or empty] +Metabolizer types: [If applicable] +isPlural: [Leave empty] +Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] +Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty] +Side effect/efficacy/other: [Specific phenotype outcome with prefix: "Side Effect:", "Efficacy:", "Other:"] +Phenotype: [Primary phenotype, e.g., "Neutropenia", "Stevens-Johnson Syndrome"] +Multiple phenotypes And/or: [EXACTLY ONE: "and", "or", or empty] +When treated with/exposed to/when assayed with: [Drug context] +Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] +Population types: [Ethnicity if specified] +Population Phenotypes or diseases: [Disease context with prefix] +Multiple phenotypes or diseases And/or: [EXACTLY ONE: "and", "or", or empty] +Comparison Allele(s) or Genotype(s): [Reference genotype] +Comparison Metabolizer types: [Reference metabolizer status] + +OUTPUT EXACTLY ONE TAB-SEPARATED ROW. +Example output format: +1449169911 HLA-B*35:08 HLA-B lamotrigine 29238301 Toxicity no The allele was not significant... HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy. *35:08 Is Not associated with likelihood of Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome or when treated with in people with Disease:Epilepsy +``` + +#### 4.4 Quality Control and Validation + +**Prompt Template**: +``` +Review this generated annotation row and validate it meets the schema requirements: + +Generated Row: {annotation_row} +Source Article Excerpt: {relevant_text} +Expected Schema: {schema_fields} + +Validation Checklist: +1. Are ALL required fields present in correct order? +2. Do controlled vocabulary fields use exact allowed values? +3. Is the variant notation standardized? +4. Are drug names generic/standard? +5. Does the sentence directly support the association? +6. Are comparison groups appropriate? +7. Is the statistical significance correctly interpreted? + +Output: +- VALID or INVALID +- List specific corrections needed +- Corrected row if invalid +``` + +### Stage 5: Multi-Row Batch Processing + +**Objective**: Process articles that contain multiple variant-outcome relationships efficiently. + +**Prompt Template**: +``` +This article contains multiple pharmacogenomic relationships. Generate separate annotation rows for each relationship. + +Article PMID: {pmid} +Article Text: {full_article} + +For each distinct variant-drug-outcome relationship found: +1. Determine annotation type (functional/drug/phenotype) +2. Generate complete TSV row following the appropriate schema +3. Ensure unique Variant Annotation IDs +4. Maintain consistency in variant nomenclature across rows + +Output format: +ANNOTATION_TYPE: [functional/drug/phenotype] +ROW: [complete tab-separated row] + +ANNOTATION_TYPE: [functional/drug/phenotype] +ROW: [complete tab-separated row] + +[Continue for all relationships found] + +Ensure no duplicate relationships and complete coverage of annotatable findings. +``` + +## Implementation Strategy + +### Phase 1: Schema Integration and Prompt Testing +1. Create detailed field-level documentation with examples from existing data +2. Test prompts with sample articles that have manual annotations +3. Validate output format compatibility with TSV import processes +4. Refine controlled vocabulary lists and standardization rules + +### Phase 2: Row-Level Validation Pipeline +1. Implement automated schema validation for generated rows +2. Create field-specific validation rules (e.g., PMID format, controlled vocabularies) +3. Build consistency checks across related annotations +4. Develop confidence scoring for generated rows + +### Phase 3: Batch Processing and Error Handling +1. Implement parallel processing for multiple articles +2. Create error recovery for malformed outputs +3. Build retry logic for low-confidence annotations +4. Develop human review queues for validation + +### Phase 4: Database Integration +1. Create direct TSV import pipelines +2. Implement deduplication logic at database level +3. Build update mechanisms for revised annotations +4. Create audit trails for annotation provenance + +## Quality Control Measures + +### Automated Row Validation +- Field count and order verification +- Controlled vocabulary compliance checking +- Cross-reference validation (PMIDs, variant IDs, drug names) +- Statistical measures plausibility checks + +### Schema Compliance Monitoring +- Real-time validation during generation +- Rejection of malformed rows with specific error messages +- Automatic retry with corrected prompts +- Escalation to human review for persistent failures + +### Consistency Enforcement +- Variant nomenclature standardization across all rows +- Drug name normalization to generic forms +- Population and phenotype term standardization +- Cross-annotation relationship validation + +## Expected Challenges and Mitigation Strategies + +### Challenge 1: Complex Multi-Field Dependencies +**Mitigation**: Use step-by-step field completion with intermediate validation checkpoints in prompts. + +### Challenge 2: Controlled Vocabulary Adherence +**Mitigation**: Provide comprehensive allowed value lists in prompts and implement strict post-processing validation. + +### Challenge 3: Tab-Separated Format Issues +**Mitigation**: Use explicit field delimiters and implement robust parsing with error detection. + +### Challenge 4: Unique ID Generation +**Mitigation**: Implement deterministic ID generation based on content hash to avoid duplicates. + +### Challenge 5: Multi-Row Consistency +**Mitigation**: Process all relationships from an article in single LLM call to maintain consistency. + +## Success Metrics + +### Schema Compliance +- Percentage of generated rows meeting exact schema requirements +- Field completion rates across different annotation types +- Controlled vocabulary adherence rates + +### Data Quality +- Accuracy of extracted variant-outcome relationships +- Consistency of annotations across multiple extractions +- Completeness of captured relationships per article + +### Processing Efficiency +- Successful row generation rate per article +- Time to generate complete annotation sets +- Human review intervention rates + +This schema-focused approach ensures that LLM outputs can be directly imported into the database structure while maintaining high data quality and consistency standards. \ No newline at end of file diff --git a/notebooks/create_pmid_annotations.ipynb b/notebooks/create_pmid_annotations.ipynb index 4fc25d8..3b7cf45 100644 --- a/notebooks/create_pmid_annotations.ipynb +++ b/notebooks/create_pmid_annotations.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 16, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -162,7 +162,7 @@ " 'meta-analysis, GWAS, replication'], dtype=object)" ] }, - "execution_count": 21, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -182,7 +182,7 @@ "28525" ] }, - "execution_count": 22, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -207,7 +207,7 @@ "34836" ] }, - "execution_count": 23, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -220,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -229,28 +229,14 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "base_json = {\n", - " \"pmid\": \"\",\n", - " \"var_drug_ann\": [],\n", - " \"var_fa_ann\": [],\n", - " \"var_pheno_ann\": []\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 27, + "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-09 11:57:58.637\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m6\u001b[0m - \u001b[1mLoaded 34836 pmid annotations from pickle\u001b[0m\n" + "100%|██████████| 34836/34836 [00:48<00:00, 713.75it/s]\n" ] } ], @@ -275,29 +261,31 @@ " # Filter by Variant Annotation Type\n", " pmid_addition = {\n", " \"pmid\": pmid,\n", + " \"pmcid\": None,\n", + " \"title\": None,\n", + " \"study_parameters\": row.to_dict(),\n", " \"var_drug_ann\": var_drug_ann_filtered,\n", " \"var_fa_ann\": var_fa_ann_filtered,\n", - " \"var_pheno_ann\": var_pheno_ann_filtered\n", + " \"var_pheno_ann\": var_pheno_ann_filtered,\n", " }\n", " pmid_table.append(pmid_addition)" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ - "# save pmid_table to jsonl\n", - "# import json\n", - "# with open(\"data/annotations_by_pmid.jsonl\", \"w\") as f:\n", - "# for pmid_addition in pmid_table:\n", - "# f.write(json.dumps(pmid_addition) + \"\\n\")" + "# save pmid_table to pickle\n", + "# pickle.dump(pmid_table, open(\"data/variantAnnotations/annotations_by_pmid.pkl\", \"wb\"))\n", + "# save pmid_table to json\n", + "# json.dump(pmid_table, open(\"data/variantAnnotations/annotations_by_pmid.json\", \"w\"))" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ @@ -306,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -322,21 +310,14 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/34836 [00:00 0 and len(pmid_addition[\"var_fa_ann\"]) == 0 and len(pmid_addition[\"var_pheno_ann\"]) == 0:\n", + " only_drug.append(pmid_addition)\n", + "len(only_drug)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "368" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "only_fa = []\n", + "for pmid_addition in annotations_by_pmcid:\n", + " if len(pmid_addition[\"var_drug_ann\"]) == 0 and len(pmid_addition[\"var_fa_ann\"]) > 0 and len(pmid_addition[\"var_pheno_ann\"]) == 0:\n", + " only_fa.append(pmid_addition)\n", + "len(only_fa)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5857" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "only_pheno = []\n", + "for pmid_addition in annotations_by_pmcid:\n", + " if len(pmid_addition[\"var_drug_ann\"]) == 0 and len(pmid_addition[\"var_fa_ann\"]) == 0 and len(pmid_addition[\"var_pheno_ann\"]) > 0:\n", + " only_pheno.append(pmid_addition)\n", + "len(only_pheno)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two Annotations and All Three" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Two annotations: 2249\n", + "All three annotations: 28\n" + ] + } + ], + "source": [ + "two_annotations = []\n", + "all_three_annotations = []\n", + "for pmid_addition in annotations_by_pmcid:\n", + " annotation_count = 0\n", + " if len(pmid_addition[\"var_drug_ann\"]) > 0:\n", + " annotation_count += 1\n", + " if len(pmid_addition[\"var_fa_ann\"]) > 0:\n", + " annotation_count += 1\n", + " if len(pmid_addition[\"var_pheno_ann\"]) > 0:\n", + " annotation_count += 1\n", + " if annotation_count == 2:\n", + " two_annotations.append(pmid_addition)\n", + " elif annotation_count == 3:\n", + " all_three_annotations.append(pmid_addition)\n", + "print(f\"Two annotations: {len(two_annotations)}\")\n", + "print(f\"All three annotations: {len(all_three_annotations)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Save all to new json files\n", + "with open(\"data/variantAnnotations/exploration/two_annotations.json\", \"w\") as f:\n", + " json.dump(two_annotations, f)\n", + "with open(\"data/variantAnnotations/exploration/all_three_annotations.json\", \"w\") as f:\n", + " json.dump(all_three_annotations, f)\n", + "with open(\"data/variantAnnotations/exploration/only_drug.json\", \"w\") as f:\n", + " json.dump(only_drug, f)\n", + "with open(\"data/variantAnnotations/exploration/only_fa.json\", \"w\") as f:\n", + " json.dump(only_fa, f)\n", + "with open(\"data/variantAnnotations/exploration/only_pheno.json\", \"w\") as f:\n", + " json.dump(only_pheno, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One of each study type" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "study_types = {\n", + " \"cohort\": [],\n", + " \"case_control\": [],\n", + " \"case_series\": [],\n", + " \"cross_sectional\": [],\n", + " \"clinical_trial\": [],\n", + " \"meta_analysis\": [],\n", + " \"GWAS\": [],\n", + " \"replication\": [],\n", + " \"prospective\": [],\n", + " \"retrospective\": [],\n", + " \"linkage\": [],\n", + " \"other\": [] \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pmid': 29238301,\n", + " 'pmcid': 'PMC5712579',\n", + " 'title': 'Association of HLA-A and HLA-B Alleles with Lamotrigine-Induced Cutaneous Adverse Drug Reactions in the Thai Population',\n", + " 'study_parameters': {'Study Parameters ID': 1449169927,\n", + " 'Variant Annotation ID': 1449169911,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 15.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'SCAR, MPE, SJS (cases and controls)',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.06,\n", + " 'Allele Of Frequency In Cases': '*35:08',\n", + " 'Frequency In Controls': 0.001,\n", + " 'Allele Of Frequency In Controls': '*35:08',\n", + " 'P Value': '= 0.231',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 10.45,\n", + " 'Confidence Interval Start': 0.4,\n", + " 'Confidence Interval Stop': 270.41,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " 'var_drug_ann': [],\n", + " 'var_fa_ann': [],\n", + " 'var_pheno_ann': [{'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan}]}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annotations_by_pmcid[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "all_study_types = set()\n", + "for annotation in annotations_by_pmcid:\n", + " study_type = annotation[\"study_parameters\"][\"Study Type\"]\n", + " all_study_types.add(study_type)\n", + "\n", + "# remove nan from all_study_types\n", + "all_study_types = list({t for t in all_study_types if pd.notna(t)})\n", + "all_study_types.sort()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GWAS\n", + "GWAS, prospective\n", + "GWAS, replication\n", + "GWAS, retrospective\n", + "case series\n", + "case series, clinical trial\n", + "case series, linkage, trios\n", + "case series, trios\n", + "case/control\n", + "case/control, GWAS\n", + "case/control, GWAS, retrospective\n", + "case/control, case series\n", + "case/control, clinical trial\n", + "case/control, clinical trial, GWAS\n", + "case/control, clinical trial, meta-analysis\n", + "case/control, clinical trial, prospective\n", + "case/control, clinical trial, retrospective\n", + "case/control, meta-analysis\n", + "case/control, meta-analysis, GWAS\n", + "case/control, prospective\n", + "case/control, replication\n", + "case/control, replication, retrospective\n", + "case/control, retrospective\n", + "clinical trial\n", + "clinical trial, GWAS\n", + "clinical trial, meta-analysis\n", + "clinical trial, meta-analysis, GWAS\n", + "clinical trial, meta-analysis, replication\n", + "clinical trial, prospective\n", + "clinical trial, replication\n", + "clinical trial, replication, prospective\n", + "clinical trial, retrospective\n", + "cohort\n", + "cohort, GWAS\n", + "cohort, GWAS, prospective\n", + "cohort, GWAS, replication\n", + "cohort, GWAS, retrospective\n", + "cohort, case series\n", + "cohort, case series, prospective\n", + "cohort, case/control\n", + "cohort, case/control, GWAS\n", + "cohort, case/control, replication\n", + "cohort, case/control, retrospective\n", + "cohort, clinical trial\n", + "cohort, clinical trial, meta-analysis\n", + "cohort, clinical trial, prospective\n", + "cohort, clinical trial, retrospective\n", + "cohort, cross sectional, retrospective\n", + "cohort, linkage\n", + "cohort, meta-analysis\n", + "cohort, meta-analysis, GWAS\n", + "cohort, meta-analysis, retrospective\n", + "cohort, prospective\n", + "cohort, prospective, retrospective\n", + "cohort, replication\n", + "cohort, replication, prospective\n", + "cohort, replication, retrospective\n", + "cohort, retrospective\n", + "cohort, trios\n", + "cross sectional\n", + "cross sectional, retrospective\n", + "linkage\n", + "linkage, trios\n", + "meta-analysis\n", + "meta-analysis, GWAS\n", + "meta-analysis, GWAS, replication\n", + "meta-analysis, GWAS, retrospective\n", + "meta-analysis, replication\n", + "meta-analysis, retrospective\n", + "prospective\n", + "prospective, retrospective\n", + "replication\n", + "replication, prospective\n", + "replication, retrospective\n", + "retrospective\n", + "retrospective, linkage, trios\n", + "trios\n" + ] + } + ], + "source": [ + "for x in all_study_types:\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Analyzing annotation study types: 100%|██████████| 13523/13523 [00:00<00:00, 1573882.37it/s]\n" + ] + } + ], + "source": [ + "annotations_by_study_type = {}\n", + "for annotation in tqdm(annotations_by_pmcid, desc=\"Analyzing annotation study types\"):\n", + " annotation_study_type = annotation[\"study_parameters\"][\"Study Type\"]\n", + " if pd.isna(annotation_study_type):\n", + " annotation_study_type = \"nan\"\n", + " if annotation_study_type not in annotations_by_study_type:\n", + " annotations_by_study_type[annotation_study_type] = []\n", + " annotations_by_study_type[annotation_study_type].append(annotation)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GWAS: 400\n", + "GWAS, prospective: 7\n", + "GWAS, replication: 3\n", + "GWAS, retrospective: 9\n", + "case series: 299\n", + "case series, clinical trial: 3\n", + "case series, linkage, trios: 1\n", + "case series, trios: 1\n", + "case/control: 1473\n", + "case/control, GWAS: 97\n", + "case/control, GWAS, retrospective: 7\n", + "case/control, case series: 1\n", + "case/control, clinical trial: 26\n", + "case/control, clinical trial, GWAS: 3\n", + "case/control, clinical trial, meta-analysis: 2\n", + "case/control, clinical trial, prospective: 27\n", + "case/control, clinical trial, retrospective: 9\n", + "case/control, meta-analysis: 58\n", + "case/control, meta-analysis, GWAS: 17\n", + "case/control, prospective: 8\n", + "case/control, replication: 30\n", + "case/control, replication, retrospective: 2\n", + "case/control, retrospective: 78\n", + "clinical trial: 502\n", + "clinical trial, GWAS: 51\n", + "clinical trial, meta-analysis: 6\n", + "clinical trial, meta-analysis, GWAS: 2\n", + "clinical trial, meta-analysis, replication: 1\n", + "clinical trial, prospective: 64\n", + "clinical trial, replication: 34\n", + "clinical trial, replication, prospective: 13\n", + "clinical trial, retrospective: 76\n", + "cohort: 6357\n", + "cohort, GWAS: 212\n", + "cohort, GWAS, prospective: 12\n", + "cohort, GWAS, replication: 2\n", + "cohort, GWAS, retrospective: 16\n", + "cohort, case series: 2\n", + "cohort, case series, prospective: 2\n", + "cohort, case/control: 69\n", + "cohort, case/control, GWAS: 2\n", + "cohort, case/control, replication: 2\n", + "cohort, case/control, retrospective: 1\n", + "cohort, clinical trial: 167\n", + "cohort, clinical trial, meta-analysis: 4\n", + "cohort, clinical trial, prospective: 6\n", + "cohort, clinical trial, retrospective: 4\n", + "cohort, cross sectional, retrospective: 4\n", + "cohort, linkage: 1\n", + "cohort, meta-analysis: 21\n", + "cohort, meta-analysis, GWAS: 9\n", + "cohort, meta-analysis, retrospective: 1\n", + "cohort, prospective: 275\n", + "cohort, prospective, retrospective: 2\n", + "cohort, replication: 36\n", + "cohort, replication, prospective: 6\n", + "cohort, replication, retrospective: 1\n", + "cohort, retrospective: 348\n", + "cohort, trios: 1\n", + "cross sectional: 19\n", + "cross sectional, retrospective: 2\n", + "linkage: 13\n", + "linkage, trios: 2\n", + "meta-analysis: 543\n", + "meta-analysis, GWAS: 90\n", + "meta-analysis, GWAS, replication: 1\n", + "meta-analysis, GWAS, retrospective: 1\n", + "meta-analysis, replication: 17\n", + "meta-analysis, retrospective: 29\n", + "nan: 1322\n", + "prospective: 270\n", + "prospective, retrospective: 6\n", + "replication: 112\n", + "replication, prospective: 1\n", + "replication, retrospective: 12\n", + "retrospective: 200\n", + "retrospective, linkage, trios: 1\n", + "trios: 9\n" + ] + } + ], + "source": [ + "# sort keys of annotations_by_study_type\n", + "annotations_by_study_type = dict(sorted(annotations_by_study_type.items()))\n", + "\n", + "# print number of annotations in each study type\n", + "for study_type, annotations in annotations_by_study_type.items():\n", + " print(f\"{study_type}: {len(annotations)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Save annotations_by_study_type to json\n", + "# with open(\"data/variantAnnotations/exploration/annotations_by_study_type.json\", \"w\") as f:\n", + "# json.dump(annotations_by_study_type, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "annotation_template = {\n", + " \"pmid\": None,\n", + " \"pmcid\": None,\n", + " \"study_parameters\": [],\n", + " \"var_drug_ann\": [],\n", + " \"var_fa_ann\": [],\n", + " \"var_pheno_ann\": [],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13523" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(annotations_by_pmcid)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Grouping annotations by PMID: 100%|██████████| 13523/13523 [00:00<00:00, 592464.33it/s]\n" + ] + } + ], + "source": [ + "annotations_pmid_grouped = {}\n", + "for annotation in tqdm(annotations_by_pmcid, desc=\"Grouping annotations by PMID\"):\n", + " annotation_pmcid = annotation[\"pmid\"]\n", + " if pd.isna(annotation_pmcid):\n", + " annotation_pmcid = \"nan\"\n", + " if annotation_pmcid not in annotations_pmid_grouped:\n", + " annotations_pmid_grouped[annotation_pmcid] = {\n", + " \"pmid\": None,\n", + " \"pmcid\": None,\n", + " \"study_parameters\": [],\n", + " \"var_drug_ann\": [],\n", + " \"var_fa_ann\": [],\n", + " \"var_pheno_ann\": [],\n", + " }\n", + " annotations_pmid_grouped[annotation_pmcid][\"pmid\"] = annotation.get(\"pmid\", None)\n", + " annotations_pmid_grouped[annotation_pmcid][\"pmcid\"] = annotation.get(\"pmcid\", None)\n", + " annotations_pmid_grouped[annotation_pmcid][\"study_parameters\"].append(annotation.get(\"study_parameters\", None))\n", + " annotations_pmid_grouped[annotation_pmcid][\"var_drug_ann\"].extend(annotation.get(\"var_drug_ann\", []))\n", + " annotations_pmid_grouped[annotation_pmcid][\"var_fa_ann\"].extend(annotation.get(\"var_fa_ann\", []))\n", + " annotations_pmid_grouped[annotation_pmcid][\"var_pheno_ann\"].extend(annotation.get(\"var_pheno_ann\", []))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pmid': 29238301,\n", + " 'pmcid': 'PMC5712579',\n", + " 'study_parameters': [{'Study Parameters ID': 1449169927,\n", + " 'Variant Annotation ID': 1449169911,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 15.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'SCAR, MPE, SJS (cases and controls)',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.06,\n", + " 'Allele Of Frequency In Cases': '*35:08',\n", + " 'Frequency In Controls': 0.001,\n", + " 'Allele Of Frequency In Controls': '*35:08',\n", + " 'P Value': '= 0.231',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 10.45,\n", + " 'Confidence Interval Start': 0.4,\n", + " 'Confidence Interval Stop': 270.41,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449170027,\n", + " 'Variant Annotation ID': 1449169930,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 15.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'SCAR, MPE or SJS (cases and controls)',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.13,\n", + " 'Allele Of Frequency In Cases': '*39:01',\n", + " 'Frequency In Controls': 0.02,\n", + " 'Allele Of Frequency In Controls': '*39:01',\n", + " 'P Value': '= 0.131',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 7.54,\n", + " 'Confidence Interval Start': 0.63,\n", + " 'Confidence Interval Stop': 89.76,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449169870,\n", + " 'Variant Annotation ID': 1449169849,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 10.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced MPE (cases and controls)',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.5,\n", + " 'Allele Of Frequency In Cases': '*15:02',\n", + " 'Frequency In Controls': 0.12,\n", + " 'Allele Of Frequency In Controls': '*15:02',\n", + " 'P Value': '= 0.005',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 7.33,\n", + " 'Confidence Interval Start': 1.63,\n", + " 'Confidence Interval Stop': 33.02,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449169858,\n", + " 'Variant Annotation ID': 1449169849,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 15.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced SCAR, SJS, MPE (cases and controls)',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.024,\n", + " 'Allele Of Frequency In Cases': '*15:02',\n", + " 'Frequency In Controls': 0.12,\n", + " 'Allele Of Frequency In Controls': '*15:02',\n", + " 'P Value': '= 0.04',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 4.89,\n", + " 'Confidence Interval Start': 1.28,\n", + " 'Confidence Interval Stop': 18.66,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449170040,\n", + " 'Variant Annotation ID': 1449169849,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 5.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced SCAR',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.2,\n", + " 'Allele Of Frequency In Cases': '*15:02',\n", + " 'Frequency In Controls': 0.12,\n", + " 'Allele Of Frequency In Controls': '*15:02',\n", + " 'P Value': '= 0.508',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 1.83,\n", + " 'Confidence Interval Start': 0.18,\n", + " 'Confidence Interval Stop': 19.25,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449169905,\n", + " 'Variant Annotation ID': 1449169896,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 10.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced MPE (cases and controls)',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.3,\n", + " 'Allele Of Frequency In Cases': '*44:03',\n", + " 'Frequency In Controls': 0.04,\n", + " 'Allele Of Frequency In Controls': '*44:03',\n", + " 'P Value': '= 0.029',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 10.29,\n", + " 'Confidence Interval Start': 1.45,\n", + " 'Confidence Interval Stop': 72.81,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449169842,\n", + " 'Variant Annotation ID': 1449169831,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 15.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced SCAR, SJS or MPE (cases and controls)',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.33,\n", + " 'Allele Of Frequency In Cases': '*02:07',\n", + " 'Frequency In Controls': 0.06,\n", + " 'Allele Of Frequency In Controls': '*02:07',\n", + " 'P Value': '= 0.013',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 7.83,\n", + " 'Confidence Interval Start': 1.6,\n", + " 'Confidence Interval Stop': 38.25,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449170031,\n", + " 'Variant Annotation ID': 1449169831,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 10.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced MPE',\n", + " 'Characteristics Type': 'Drug',\n", + " 'Frequency In Cases': 0.3,\n", + " 'Allele Of Frequency In Cases': '*02:07',\n", + " 'Frequency In Controls': 0.06,\n", + " 'Allele Of Frequency In Controls': '*02:07',\n", + " 'P Value': '= 0.052',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 6.71,\n", + " 'Confidence Interval Start': 1.13,\n", + " 'Confidence Interval Stop': 40.07,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449170033,\n", + " 'Variant Annotation ID': 1449169831,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 5.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced SCAR',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.4,\n", + " 'Allele Of Frequency In Cases': '*02:07',\n", + " 'Frequency In Controls': 0.22,\n", + " 'Allele Of Frequency In Controls': '*02:07',\n", + " 'P Value': '= 0.060',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 10.44,\n", + " 'Confidence Interval Start': 1.23,\n", + " 'Confidence Interval Stop': 88.44,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449169890,\n", + " 'Variant Annotation ID': 1449169878,\n", + " 'Study Type': 'case series',\n", + " 'Study Cases': 10.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced MPE (cases and controls)',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.7,\n", + " 'Allele Of Frequency In Cases': '*33:03',\n", + " 'Frequency In Controls': 0.22,\n", + " 'Allele Of Frequency In Controls': '*33:03',\n", + " 'P Value': '= 0.005',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 8.27,\n", + " 'Confidence Interval Start': 1.83,\n", + " 'Confidence Interval Stop': 37.41,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301},\n", + " {'Study Parameters ID': 1449170038,\n", + " 'Variant Annotation ID': 1449169878,\n", + " 'Study Type': 'case/control',\n", + " 'Study Cases': 5.0,\n", + " 'Study Controls': 50.0,\n", + " 'Characteristics': 'LTG-induced SCAR',\n", + " 'Characteristics Type': 'Disease',\n", + " 'Frequency In Cases': 0.0,\n", + " 'Allele Of Frequency In Cases': '*33:03',\n", + " 'Frequency In Controls': 0.22,\n", + " 'Allele Of Frequency In Controls': '*33:03',\n", + " 'P Value': '= 0.570',\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': 0.31,\n", + " 'Confidence Interval Start': 0.02,\n", + " 'Confidence Interval Stop': 6.08,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 29238301}],\n", + " 'var_drug_ann': [],\n", + " 'var_fa_ann': [],\n", + " 'var_pheno_ann': [{'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169911,\n", + " 'Variant/Haplotypes': 'HLA-B*35:08',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (1/15) and controls (individuals without AEs who took lamotrigine) (0/50). The allele was significant when comparing between cases (1/15) and the general population (1/986).',\n", + " 'Sentence': 'HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*35:08',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169930,\n", + " 'Variant/Haplotypes': 'HLA-B*39:01',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'no',\n", + " 'Notes': 'The allele was not significant when comparing allele frequency in cases of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome (SJS) and Maculopapular Exanthema (MPE) (2/15) and controls (controls were individuals without AEs who took lamotrigine)(1/50) both on lamotrigine.',\n", + " 'Sentence': 'HLA-B *39:01 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*39:01',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169849,\n", + " 'Variant/Haplotypes': 'HLA-B*15:02',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was a significant difference in allele frequency when comparing incidence of LTG-induced severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE) and controls (individuals without AEs who took lamotrigine). There was also a significant difference when only considering MPE. The allele was more frequent in cases of MPE (6/15) than in controls (6/50).',\n", + " 'Sentence': 'HLA-B *15:02 is associated with increased likelihood of Maculopapular Exanthema or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*15:02',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Side Effect:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169896,\n", + " 'Variant/Haplotypes': 'HLA-B*44:03',\n", + " 'Gene': 'HLA-B',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was significantly different when comparing between cases of LTG-induced MPE (3/10) and controls (individuals without AEs who took lamotrigine) (2/50).',\n", + " 'Sentence': 'HLA-B *44:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*44:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169831,\n", + " 'Variant/Haplotypes': 'HLA-A*02:07',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'The allele was more frequent in cases (5/15) than in controls (3/50). The allele was only significant when the authors group together severe cutaneous adverse reactions (SCAR), Stevens-Johnson Syndrome or Maculopapular Exanthema (MPE). The allele was not significantly associated with MPE, SCAR or SJS alone.',\n", + " 'Sentence': 'HLA-A *02:07 is associated with increased risk of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*02:07',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'risk of',\n", + " 'Phenotype': 'Side Effect:Maculopapular Exanthema, Side Effect:Severe Cutaneous Adverse Reactions, Side Effect:Stevens-Johnson Syndrome',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1449169878,\n", + " 'Variant/Haplotypes': 'HLA-A*33:03',\n", + " 'Gene': 'HLA-A',\n", + " 'Drug(s)': 'lamotrigine',\n", + " 'PMID': 29238301,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': 'There was no significant difference in allele frequency when comparing between individuals who experienced lamotrigine induced severe cutaneous adverse reactions (SCAR), maculopapular exanethema (MPE), or Stevens Johnson Syndrome (SJS) (7/15) versus controls (took lamotrigine without AEs) (11/50) but was significant when comparing cases of LTG-induced MPE (7/10) and controls (11/50). It was not associated with SCAR.',\n", + " 'Sentence': 'HLA-A *33:03 is associated with increased likelihood of Maculopapular Exanthema when exposed to lamotrigine in people with Epilepsy.',\n", + " 'Alleles': '*33:03',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'likelihood of',\n", + " 'Phenotype': 'Disease:Maculopapular Exanthema',\n", + " 'Multiple phenotypes And/or': 'or',\n", + " 'When treated with/exposed to/when assayed with': 'when exposed to',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Disease:Epilepsy',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan}]}" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annotations_pmid_grouped[29238301]" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# Save annotations_pmid_grouped to json\n", + "with open(\"data/variantAnnotations/exploration/annotations_pmid_grouped_2.json\", \"w\") as f:\n", + " json.dump(annotations_pmid_grouped, f, indent=2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of pmids in annotations_pmid_grouped: 2864\n", + "Number of pmids in annotations_by_pmcid: 13523\n", + "Number of pmids in annotations_by_pmcid: 13523\n" + ] + } + ], + "source": [ + "# Check number of pmids in annotations_pmid_grouped\n", + "print(f\"Number of pmids in annotations_pmid_grouped: {len(annotations_pmid_grouped)}\")\n", + "# Check number of pmids in annotations_by_pmcid\n", + "print(f\"Number of pmids in annotations_by_pmcid: {len(annotations_by_pmcid)}\")\n", + "# Check number of pmids in annotations_by_pmcid\n", + "print(f\"Number of pmids in annotations_by_pmcid: {len(annotations_by_pmcid)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pmid': 39528547,\n", + " 'pmcid': 'PMC11554802',\n", + " 'title': 'Efficacy and safety of sacituzumab govitecan Trop-2-targeted antibody-drug conjugate in solid tumors and UGT1A1*28 polymorphism: a systematic review and meta-analysis',\n", + " 'study_parameters': {'Study Parameters ID': 1453086511,\n", + " 'Variant Annotation ID': 1453086503,\n", + " 'Study Type': 'meta-analysis',\n", + " 'Study Cases': 1138.0,\n", + " 'Study Controls': nan,\n", + " 'Characteristics': 'Grade 3/4 Neutropenia, 5 studies',\n", + " 'Characteristics Type': 'Study Cohort',\n", + " 'Frequency In Cases': nan,\n", + " 'Allele Of Frequency In Cases': nan,\n", + " 'Frequency In Controls': nan,\n", + " 'Allele Of Frequency In Controls': nan,\n", + " 'P Value': '= 0.05',\n", + " 'Ratio Stat Type': nan,\n", + " 'Ratio Stat': nan,\n", + " 'Confidence Interval Start': nan,\n", + " 'Confidence Interval Stop': nan,\n", + " 'Biogeographical Groups': 'Multiple groups',\n", + " 'PMID': 39528547},\n", + " 'var_drug_ann': [],\n", + " 'var_fa_ann': [],\n", + " 'var_pheno_ann': [{'Variant Annotation ID': 1453086503,\n", + " 'Variant/Haplotypes': 'UGT1A1*1, UGT1A1*28',\n", + " 'Gene': 'UGT1A1',\n", + " 'Drug(s)': 'sacituzumab govitecan',\n", + " 'PMID': 39528547,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'yes',\n", + " 'Notes': '\"Genotype-specific analyses revealed higher incidence of grade 3–4 neutropenia in patients with heterozygous and homozygous variant genotypes versus wild type group. Severe neutropenia occurred in 67% of homozygous carriers versus 44% from wild type and 50% from heterozygous groups respectively (P\\u2009=\\u20090.05, Supplementary Table 3). \"',\n", + " 'Sentence': 'UGT1A1 *1/*28 + *28/*28 is associated with increased severity of Neutropenia when treated with sacituzumab govitecan in people with Breast Neoplasms as compared to UGT1A1 *1/*1.',\n", + " 'Alleles': '*1/*28 + *28/*28',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': nan,\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': 'increased',\n", + " 'Side effect/efficacy/other': 'severity of',\n", + " 'Phenotype': 'Side Effect:Neutropenia',\n", + " 'Multiple phenotypes And/or': nan,\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': 'in people with',\n", + " 'Population Phenotypes or diseases': 'Other:Breast Neoplasms',\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': '*1/*1',\n", + " 'Comparison Metabolizer types': nan}]}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annotations_by_pmcid[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_annotations_by_pmcid = sorted(annotations_by_pmcid, key=lambda x: x[\"pmid\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pmid': 990860,\n", + " 'pmcid': 'PMC1689719',\n", + " 'title': 'Drug-induced haemolysis in glucose-6-phosphate dehydrogenase deficiency',\n", + " 'study_parameters': {'Study Parameters ID': 1184521221,\n", + " 'Variant Annotation ID': 1184521219,\n", + " 'Study Type': 'cohort',\n", + " 'Study Cases': 8.0,\n", + " 'Study Controls': nan,\n", + " 'Characteristics': 'Red blood cells from G6PD deficient individuals classified as having the Canton variant or Hong Kong-Pokfulam variant.',\n", + " 'Characteristics Type': 'Study Cohort',\n", + " 'Frequency In Cases': nan,\n", + " 'Allele Of Frequency In Cases': nan,\n", + " 'Frequency In Controls': nan,\n", + " 'Allele Of Frequency In Controls': nan,\n", + " 'P Value': nan,\n", + " 'Ratio Stat Type': 'OR',\n", + " 'Ratio Stat': nan,\n", + " 'Confidence Interval Start': nan,\n", + " 'Confidence Interval Stop': nan,\n", + " 'Biogeographical Groups': 'East Asian',\n", + " 'PMID': 990860},\n", + " 'var_drug_ann': [],\n", + " 'var_fa_ann': [],\n", + " 'var_pheno_ann': [{'Variant Annotation ID': 1184521237,\n", + " 'Variant/Haplotypes': 'G6PD deficiency',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'phenytoin',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD deficiency is not associated with Hemolysis when treated with phenytoin.',\n", + " 'Alleles': nan,\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184519163,\n", + " 'Variant/Haplotypes': 'G6PD deficiency',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'primaquine',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from 10 G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment. Primaquine treatment was associated with decreased half life in all cases, shortening it to 3-5 days. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD deficiency is associated with Hemolysis when treated with primaquine.',\n", + " 'Alleles': nan,\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521196,\n", + " 'Variant/Haplotypes': 'G6PD deficiency',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'chloramphenicol',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD deficiency is not associated with Hemolysis when treated with chloramphenicol.',\n", + " 'Alleles': nan,\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521202,\n", + " 'Variant/Haplotypes': 'G6PD deficiency',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'aminosalicylic acid, isoniazid, streptomycin',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*. 4-Aminosalicylic acid = PAS.',\n", + " 'Sentence': 'G6PD deficiency is not associated with Hemolysis when treated with aminosalicylic acid, isoniazid or streptomycin.',\n", + " 'Alleles': nan,\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': 'or',\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521224,\n", + " 'Variant/Haplotypes': 'G6PD deficiency',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'levodopa',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD deficiency is not associated with Hemolysis when treated with levodopa.',\n", + " 'Alleles': nan,\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521219,\n", + " 'Variant/Haplotypes': 'G6PD deficiency',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'trimethoprim',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD deficiency is not associated with Hemolysis when treated with trimethoprim.',\n", + " 'Alleles': nan,\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521208,\n", + " 'Variant/Haplotypes': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'menadione sodium bisulfite',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- menaphthone sodium bisulphite (Hykinone) treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like (assigned as deficiency phenotype) is not associated with Hemolysis when treated with menadione sodium bisulfite.',\n", + " 'Alleles': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521229,\n", + " 'Variant/Haplotypes': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'trihexyphenidyl',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- benzhexol treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like (assigned as deficiency phenotype) is not associated with Hemolysis when treated with trihexyphenidyl.',\n", + " 'Alleles': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521141,\n", + " 'Variant/Haplotypes': 'G6PD deficiency',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'sulfamethoxazole',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'in some but not all cases. Red blood cells from 12 G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment. Only in 4 cases was the half life shortened by sulphamethoxazole treatment. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD deficiency is associated with Hemolysis when treated with sulfamethoxazole.',\n", + " 'Alleles': nan,\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521135,\n", + " 'Variant/Haplotypes': 'G6PD deficiency',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'nitrofurantoin',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from 3 G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment. Nitrofurantoin treatment was associated with decreased half life in all cases, shortening it to 4-14 days. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD deficiency is associated with Hemolysis when treated with nitrofurantoin.',\n", + " 'Alleles': nan,\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521157,\n", + " 'Variant/Haplotypes': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'proguanil',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from a G6PD deficient patient were labelled with 51-Cr and cross-transfused into a G6PD-normal male recipient. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like (assigned as deficiency phenotype) is not associated with Hemolysis when treated with proguanil.',\n", + " 'Alleles': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521147,\n", + " 'Variant/Haplotypes': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'chloroquine',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from a G6PD deficient patient were labelled with 51-Cr and cross-transfused into a G6PD-normal male recipient. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like (assigned as deficiency phenotype) is not associated with Hemolysis when treated with chloroquine.',\n", + " 'Alleles': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521173,\n", + " 'Variant/Haplotypes': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'phenylbutazone',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like (assigned as deficiency phenotype) is not associated with Hemolysis when treated with phenylbutazone.',\n", + " 'Alleles': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521162,\n", + " 'Variant/Haplotypes': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'pyrimethamine',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from a G6PD deficient patient were labelled with 51-Cr and cross-transfused into a G6PD-normal male recipient. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like (assigned as deficiency phenotype) is not associated with Hemolysis when treated with pyrimethamine.',\n", + " 'Alleles': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521179,\n", + " 'Variant/Haplotypes': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'colchicine, probenecid',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from G6PD deficient patients were labelled with 51-Cr and cross-transfused into G6PD-normal male recipients. The half-life of these labelled red blood cells was determined before and after drug treatment- drug treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like (assigned as deficiency phenotype) is not associated with Hemolysis when treated with colchicine or probenecid.',\n", + " 'Alleles': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': 'or',\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan},\n", + " {'Variant Annotation ID': 1184521167,\n", + " 'Variant/Haplotypes': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Gene': 'G6PD',\n", + " 'Drug(s)': 'acetaminophen',\n", + " 'PMID': 990860,\n", + " 'Phenotype Category': 'Toxicity',\n", + " 'Significance': 'not stated',\n", + " 'Notes': 'Red blood cells from a G6PD deficient patient were labelled with 51-Cr and cross-transfused into a G6PD-normal male recipient. The half-life of these labelled red blood cells was determined before and after drug treatment- paracetamol treatment was not associated with decreased half life. *Note: genotyping to determine underlying G6PD deficient variant was not carried out*.',\n", + " 'Sentence': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like (assigned as deficiency phenotype) is not associated with Hemolysis when treated with acetaminophen.',\n", + " 'Alleles': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'Specialty Population': nan,\n", + " 'Metabolizer types': 'deficiency',\n", + " 'isPlural': 'Is',\n", + " 'Is/Is Not associated': 'Not associated with',\n", + " 'Direction of effect': nan,\n", + " 'Side effect/efficacy/other': nan,\n", + " 'Phenotype': 'Side Effect:Hemolysis',\n", + " 'Multiple phenotypes And/or': 'and',\n", + " 'When treated with/exposed to/when assayed with': 'when treated with',\n", + " 'Multiple drugs And/or': nan,\n", + " 'Population types': nan,\n", + " 'Population Phenotypes or diseases': nan,\n", + " 'Multiple phenotypes or diseases And/or': nan,\n", + " 'Comparison Allele(s) or Genotype(s)': nan,\n", + " 'Comparison Metabolizer types': nan}]}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_annotations_by_pmcid[5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "default", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb new file mode 100644 index 0000000..2f91d52 --- /dev/null +++ b/notebooks/test.ipynb @@ -0,0 +1,675 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell: \n", + "# The lines below will instruct jupyter to reload imported modules before \n", + "# executing code cells. This enables you to quickly iterate and test revisions\n", + "# to your code without having to restart the kernel and reload all of your \n", + "# modules each time you make a code change in a separate python file.\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from src.annotation_extraction.simple_inference import SimpleLLM, PromptGenerator\n", + "from src.annotation_extraction.models import ArticleParser" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-11 15:12:58.134\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m73\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-11 15:12:58.137\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m73\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n" + ] + } + ], + "source": [ + "article_title = ArticleParser(pmcid=\"PMC11730665\").parse().title\n", + "article_text = ArticleParser(pmcid=\"PMC11730665\").parse().article_text" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "DRUG_EXTRACTION_PROMPT = \"\"\"\n", + "You are an expert pharmacogenomics researcher reading and extracting annotations from the following article\n", + "\n", + "\\n\\n{article_text}\\n\\n\n", + "\n", + "These are the following terms for which we need to extract values:\n", + "\n", + "Term: Variant/Haplotypes\n", + "- Content: The specific genetic variant mentioned in the study\n", + "- Manual Process: Look for SNP IDs (rs numbers), star alleles (CYP2D6*4), or genotype combinations\n", + "- Example: rs2909451, CYP2C19*1, CYP2C19*2, *1/*18\n", + "\n", + "Term: Gene\n", + "- Content: Gene symbol associated with the variant\n", + "- Manual Process: Find the gene name near the variant mention, use standard HUGO symbols\n", + "- Example: DPP4, CYP2C19, KCNJ11\n", + "\n", + "Term: Drug(s)\n", + "- Content: Generic drug name(s) studied\n", + "- Manual Process: Extract drug names from methods/results, use generic names, separate multiple drugs with commas\n", + "- Example: sitagliptin, clopidogrel, aspirin\n", + "\n", + "Term: Phenotype Category\n", + "- Content: Type of clinical outcome studied\n", + "- Manual Process: Categorize based on what was measured:\n", + " - Efficacy: Treatment response, clinical improvement\n", + " - Metabolism/PK: Drug levels, clearance, half-life\n", + " - Toxicity: Adverse events, side effects\n", + " - Dosage: Dose requirements, dose adjustments\n", + " - Other: Everything else\n", + "- Example: Efficacy (for HbA1c improvement study)\n", + "\n", + "Term: Significance\n", + "- Content: Whether the association was statistically significant\n", + "- Manual Process: Look for p-values, confidence intervals:\n", + " - yes: p < 0.05 or explicitly stated as significant\n", + " - no: p ≥ 0.05 or stated as non-significant\n", + " - not stated: No statistical testing mentioned\n", + "- Example: yes (P < .001 in sitagliptin study)\n", + "\n", + "Term: Notes\n", + "- Content: Key study details, methodology, or important context\n", + "- Manual Process: Extract relevant quotes showing statistical results, study design, or important caveats\n", + "- Example: \"Patients with the rs2909451 TT genotype in the study group exhibited a median HbA1c improvement of 0.57...\"\n", + "\n", + "Term: Standardized Sentence\n", + "\n", + "- Content: Standardized description of the genetic association\n", + "- Manual Process: Write in format: \"[Genotype/Allele] is [associated with/not associated with] [increased/decreased]\n", + "[outcome] [drug context] [population context]\"\n", + "- Example: \"Genotype TT is associated with decreased response to sitagliptin in people with Diabetes Mellitus, Type 2.\"\n", + "\n", + "Term: Alleles\n", + "\n", + "- Content: Specific allele or genotype if different from Variant/Haplotypes field\n", + "- Manual Process: Extract the exact genotype mentioned (AA, TT, CC, del/del, etc.)\n", + "- Example: TT, *1/*18, del/del\n", + "\n", + "Term: Metabolizer types\n", + "\n", + "- Content: CYP enzyme phenotype categories\n", + "- Manual Process: Look for metabolizer classifications in CYP studies:\n", + " - poor metabolizer, intermediate metabolizer, extensive metabolizer, ultrarapid metabolizer\n", + "- Example: intermediate metabolizer\n", + "\n", + "Term: Comparison Allele(s) or Genotype(s)\n", + "\n", + "- Content: Reference genotype used for comparison\n", + "- Manual Process: Find what the study variant was compared against\n", + "- Example: *1/*1, C (for wild-type comparisons)\n", + "\n", + "Term: Comparison Metabolizer types\n", + "\n", + "- Content: Reference metabolizer status for comparison\n", + "- Manual Process: Extract the comparison metabolizer phenotype\n", + "- Example: normal metabolizer\n", + "\n", + "Term: Specialty Population\n", + "\n", + "- Content: Age-specific populations\n", + "- Manual Process: Check if study specifically focused on:\n", + " - Pediatric: Children/adolescents\n", + " - Geriatric: Elderly patients\n", + " - Leave empty for general adult populations\n", + "\n", + "Term: Population types\n", + "- Content: Descriptor of study population\n", + "- Manual Process: Look for population descriptors, usually \"in people with\" or ethnicity information\n", + "- Example: in people with\n", + "\n", + "Term: Population Phenotypes or diseases\n", + "- Content: Disease/condition context with standardized prefix\n", + "- Manual Process: Find the medical condition studied, add appropriate prefix:\n", + " - Disease: for established diseases\n", + " - Other: for conditions/traits\n", + " - Side Effect: for adverse events\n", + "- Example: Other:Diabetes Mellitus, Type 2\n", + "\n", + "Term: isPlural\n", + "- Content: Grammar helper for sentence construction\n", + "- Manual Process: Use Is for singular subjects, Are for plural\n", + "- Example: Is\n", + "\n", + "Term: Is/Is Not associated\n", + "- Content: Direction of association\n", + "- Manual Process: Determine if association was:\n", + " - Associated with: Positive association found\n", + " - Not associated with: No association found\n", + "- Example: Associated with\n", + "\n", + "Term: Direction of effect\n", + "\n", + "- Content: Whether the effect increases or decreases the outcome\n", + "- Manual Process: Look for directional language:\n", + " - increased: Higher levels, better response, more effect\n", + " - decreased: Lower levels, worse response, less effect\n", + " - Leave empty if no clear direction\n", + "- Example: decreased\n", + "\n", + "Term: PD/PK terms\n", + "\n", + "- Content: Pharmacological outcome descriptor\n", + "- Manual Process: Extract the specific outcome measured:\n", + " - response to, concentrations of, metabolism of, clearance of, dose of\n", + "- Example: response to\n", + "\n", + "Term: Multiple drugs And/or\n", + "\n", + "- Content: Logical connector for multiple drugs\n", + "- Manual Process: If multiple drugs mentioned:\n", + " - and: All drugs together\n", + " - or: Any of the drugs\n", + " - Leave empty for single drug\n", + "\n", + "Term: Multiple phenotypes or diseases And/or\n", + "\n", + "- Content: Logical connector for multiple conditions\n", + "- Manual Process: Similar to drugs, use and/or for multiple conditions\n", + "- Leave empty for single condition\n", + "\n", + "General recommended strategies\n", + "\n", + "1. Scan for genetic variants: Look for \"rs\" numbers, gene names with asterisks, or phrases like \"genotype,\" \"allele,\"\n", + "\"polymorphism\"\n", + "2. Identify drug context: Find drug names in methods, results, or discussion sections\n", + "3. Locate outcome measures: Look for clinical endpoints, lab values, response rates, adverse events\n", + "4. Find statistical associations: Search for p-values, odds ratios, significant differences between genotype groups\n", + "5. Extract population details: Note the study population, disease context, and inclusion criteria\n", + "6. Standardize the relationship: Convert the finding into the standardized sentence format following the association pattern\n", + "\n", + "For each term, the output should be of the format:\n", + "\n", + "Extracted Output: (output)\n", + "Reason: (one sentence justification)\n", + "Quote: (quote from the article that demonstrates why)\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "PHENO_EXTRACTION_PROMPT = \"\"\"\n", + "You are an expert pharmacogenomics researcher reading and extracting annotations related to how variants affect phenotypes from the following article\n", + "\n", + "\\n\\n{article_text}\\n\\n\n", + "\n", + "These are the following terms for which we need to extract values:\n", + "\n", + "## Terms for Extraction\n", + "\n", + "### Variant/Haplotypes\n", + "- **Content**: The specific genetic variant studied\n", + "- **Manual Process**: Extract SNP IDs (rs numbers), HLA alleles, star alleles, or genotype combinations\n", + "- **Example**: HLA-B*35:08, rs1801272, UGT1A1*1, UGT1A1*28\n", + "\n", + "### Gene\n", + "- **Content**: Gene symbol associated with the variant\n", + "- **Manual Process**: Find the gene name near the variant mention\n", + "- **Example**: HLA-B, CYP2A6, UGT1A1\n", + "\n", + "### Drug(s)\n", + "- **Content**: Drug(s) that caused or were involved in the phenotype\n", + "- **Manual Process**: \n", + " - Extract drug names that triggered the adverse event or phenotype\n", + " - Leave empty for disease susceptibility studies without drug involvement\n", + "- **Example**: lamotrigine, sacituzumab govitecan, empty for disease predisposition\n", + "\n", + "### Phenotype Category\n", + "- **Content**: Type of phenotype or outcome studied\n", + "- **Manual Process**: Categorize based on primary outcome:\n", + " - Toxicity: Adverse drug reactions, side effects, drug-induced toxicity\n", + " - Efficacy: Treatment response, therapeutic outcomes\n", + " - Metabolism/PK: Pharmacokinetic parameters, drug levels\n", + " - Dosage: Dose requirements, dose-response relationships\n", + " - Other: Disease susceptibility, traits not directly drug-related\n", + "- **Example**: \n", + " - Toxicity (for Stevens-Johnson Syndrome)\n", + " - Other (for alcoholism risk)\n", + "\n", + "### Significance\n", + "- **Content**: Statistical significance of the association\n", + "- **Manual Process**: Look for p-values and statistical tests:\n", + " - yes: p < 0.05 or stated as significant\n", + " - no: p ≥ 0.05 or explicitly non-significant\n", + " - not stated: No statistical testing reported\n", + "- **Example**: no (for non-significant HLA associations)\n", + "\n", + "### Notes\n", + "- **Content**: Key study details, statistics, methodology\n", + "- **Manual Process**: Extract relevant quotes showing statistical results, case descriptions, or important context\n", + "- **Example**: \"The allele was not significant when comparing allele frequency in cases...\"\n", + "\n", + "### Standardized Sentence\n", + "- **Content**: Standardized description of the genetic-phenotype association\n", + "- **Manual Process**: Write in format: \"[Variant] is [associated with/not associated with] [increased/decreased] [phenotype outcome] [drug context] [population context]\"\n", + "- **Example**: \"HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.\"\n", + "\n", + "### Alleles\n", + "- **Content**: Specific allele or genotype if different from main variant field\n", + "- **Manual Process**: Extract the exact genotype mentioned\n", + "- **Example**: *35:08, AA + AT, *1/*28 + *28/*28\n", + "\n", + "### Specialty Population\n", + "- **Content**: Age-specific populations\n", + "- **Manual Process**: Identify if study focused on specific age groups:\n", + " - Pediatric: Children/adolescents\n", + " - Geriatric: Elderly patients\n", + " - Leave empty for general adult populations\n", + "- **Example**: Pediatric (for children with Fanconi Anemia)\n", + "\n", + "### Metabolizer Types\n", + "- **Content**: CYP enzyme phenotype when applicable\n", + "- **Manual Process**: Look for metabolizer classifications in CYP studies:\n", + " - poor metabolizer\n", + " - intermediate metabolizer\n", + " - extensive metabolizer\n", + " - ultrarapid metabolizer\n", + " - deficiency\n", + "- **Example**: ultrarapid metabolizer, intermediate activity\n", + "\n", + "### isPlural\n", + "- **Content**: Grammar helper for sentence construction\n", + "- **Manual Process**: Use Is for singular subjects, Are for plural\n", + "- **Example**: Is (for single allele), Are (for combined genotypes)\n", + "\n", + "### Is/Is Not Associated\n", + "- **Content**: Direction of statistical association\n", + "- **Manual Process**: Determine association type:\n", + " - Associated with: Positive association found\n", + " - Not associated with: No association found\n", + "- **Example**: Not associated with, Associated with\n", + "\n", + "### Direction of Effect\n", + "- **Content**: Whether the variant increases or decreases the phenotype\n", + "- **Manual Process**: Look for directional language:\n", + " - increased: Higher risk, more severe, greater likelihood\n", + " - decreased: Lower risk, less severe, reduced likelihood\n", + " - Leave empty if no clear direction\n", + "- **Example**: \n", + " - increased (for higher toxicity risk)\n", + " - decreased (for lower disease risk)\n", + "\n", + "### Side Effect/Efficacy/Other\n", + "- **Content**: Specific phenotype outcome with standardized prefix\n", + "- **Manual Process**: Categorize the phenotype and add appropriate prefix:\n", + " - Side Effect: for adverse drug reactions\n", + " - Efficacy: for therapeutic outcomes\n", + " - Disease: for disease conditions\n", + " - Other: for other traits/conditions\n", + " - PK: for pharmacokinetic measures\n", + "- **Example**: \n", + " - Side Effect:Stevens-Johnson Syndrome\n", + " - Disease:Alcohol abuse\n", + " - Other:Medication adherence\n", + "\n", + "### When Treated With/Exposed To/When Assayed With\n", + "- **Content**: Drug administration context\n", + "- **Manual Process**: Use standard phrases:\n", + " - when treated with: For therapeutic drug administration\n", + " - when exposed to: For environmental or non-therapeutic exposure\n", + " - due to: For substance-related disorders\n", + " - Leave empty for non-drug phenotypes\n", + "- **Example**: when treated with, due to (for substance abuse)\n", + "\n", + "### Multiple Drugs And/Or\n", + "- **Content**: Logical connector for multiple drugs\n", + "- **Manual Process**: If multiple drugs involved:\n", + " - and: Combination therapy\n", + " - or: Any of the drugs\n", + " - Leave empty for single drug\n", + "- **Example**: or (for any of several drugs)\n", + "\n", + "### Population Types\n", + "- **Content**: Description of study population\n", + "- **Manual Process**: Look for population descriptors:\n", + " - in people with: General population with condition\n", + " - in children with: Pediatric population\n", + " - in women with: Gender-specific population\n", + "- **Example**: in people with, in children with\n", + "\n", + "### Population Phenotypes or Diseases\n", + "- **Content**: Disease/condition context with prefix\n", + "- **Manual Process**: Find the medical condition and add prefix:\n", + " - Disease: for established diseases\n", + " - Other: for conditions/traits\n", + "- **Example**: \n", + " - Disease:Epilepsy\n", + " - Other:Diabetes Mellitus, Type 2\n", + "\n", + "### Multiple Phenotypes or Diseases And/Or\n", + "- **Content**: Logical connector for multiple conditions\n", + "- **Manual Process**: Use and/or for multiple disease contexts\n", + "- **Example**: and (for multiple comorbidities)\n", + "\n", + "### Comparison Allele(s) or Genotype(s)\n", + "- **Content**: Reference genotype for comparison\n", + "- **Manual Process**: Find what the variant was compared against\n", + "- **Example**: TT (wild-type), *1/*1 (normal function allele)\n", + "\n", + "### Comparison Metabolizer Types\n", + "- **Content**: Reference metabolizer phenotype\n", + "- **Manual Process**: Extract comparison metabolizer status\n", + "- **Example**: normal metabolizer\n", + "\n", + "## General Strategy Recommendations\n", + "\n", + "1. **Identify Phenotype Outcomes**: Look for adverse events, toxicities, disease conditions, clinical traits\n", + "2. **Find Genetic Associations**: Search for variants linked to the phenotype (may or may not involve drugs)\n", + "3. **Determine Drug Involvement**: Check if phenotype is drug-induced or related to disease susceptibility\n", + "4. **Extract Statistical Evidence**: Look for odds ratios, p-values, case reports, frequency differences\n", + "5. **Categorize Phenotype Type**: Classify as toxicity, efficacy, disease susceptibility, or other trait\n", + "6. **Note Population Context**: Identify specific patient populations, age groups, disease conditions\n", + "7. **Standardize the Relationship**: Convert findings into standardized sentence format describing the genetic-phenotype association\n", + "\n", + "For each term, the output should be of the format:\n", + "\n", + "Extracted Output: (output)\n", + "Reason: (one sentence justification)\n", + "Quote: (quote from the article that demonstrates why)\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "FA_EXTRACTION_PROMPT = \"\"\"\n", + "You are an expert pharmacogenomics researcher reading and extracting annotations related to how variants affect in-vitro or lab measured mechanisms from the following article\n", + "\n", + "\\n\\n{article_text}\\n\\n\n", + "\n", + "These are the following terms for which we need to extract values:\n", + "\n", + "## Terms for Extraction\n", + "\n", + "### Variant/Haplotypes\n", + "- **Content**: The specific genetic variant studied\n", + "- **Manual Process**: Extract variant names, star alleles, SNP IDs, or protein constructs tested\n", + "- **Example**: CYP2C19*1, CYP2C19*17, rs72552763, CYP2B6*1, CYP2B6*6\n", + "\n", + "### Gene\n", + "- **Content**: Gene symbol associated with the variant\n", + "- **Manual Process**: Identify the gene being studied functionally\n", + "- **Example**: CYP2C19, CYP2B6, SLC22A1\n", + "\n", + "### Drug(s)\n", + "- **Content**: Substrate or compound used in the functional assay\n", + "- **Manual Process**: Extract the drug/substrate used to test enzyme activity or transport\n", + "- **Example**: normeperidine, bupropion, warfarin, voriconazole\n", + "\n", + "### Phenotype Category\n", + "- **Content**: Type of functional outcome measured\n", + "- **Manual Process**: Categorize based on what was measured:\n", + " - Metabolism/PK: Enzyme activity, clearance, transport, binding affinity\n", + " - Efficacy: Functional response in cellular systems\n", + " - Leave empty for basic biochemical studies\n", + "- **Example**: \n", + " - Metabolism/PK (for enzyme kinetics)\n", + " - Efficacy (for cellular response)\n", + "\n", + "### Significance\n", + "- **Content**: Statistical significance of functional differences\n", + "- **Manual Process**: Look for statistical comparisons:\n", + " - yes: Significant differences in activity/function\n", + " - no: No significant differences\n", + " - not stated: No statistical testing reported\n", + "- **Example**: \n", + " - yes (for significant activity differences)\n", + " - not stated (for descriptive studies)\n", + "\n", + "### Notes\n", + "- **Content**: Key experimental details, methodology, quantitative results\n", + "- **Manual Process**: Extract relevant quotes showing experimental conditions, numerical results, or important technical details\n", + "- **Example**: \"Clearance was 26.57% of wild-type. CYP2C19 variants expressed in Sf21 insect cells...\"\n", + "\n", + "### Standardized Sentence\n", + "- **Content**: Standardized description of the functional relationship\n", + "- **Manual Process**: Write in format: \"[Variant] is associated with [increased/decreased] [functional outcome] [experimental context] as compared to [reference variant]\"\n", + "- **Example**: \"CYP2C19 *17/*17 is associated with increased formation of normeperidine as compared to CYP2C19 *1/*1 + *1/*17.\"\n", + "\n", + "### Alleles\n", + "- **Content**: Specific allele or genotype tested\n", + "- **Manual Process**: Extract the exact variant designation\n", + "- **Example**: *17/*17, *1/*1, del, A\n", + "\n", + "### Metabolizer Types\n", + "- **Content**: Phenotype classification if applicable\n", + "- **Manual Process**: Rarely used in functional studies; mainly for CYP phenotyping\n", + "- **Example**: Usually empty\n", + "\n", + "### Comparison Allele(s) or Genotype(s)\n", + "- **Content**: Reference variant for comparison\n", + "- **Manual Process**: Find the control/wild-type variant used for comparison\n", + "- **Example**: *1/*1 + *1/*17, *1, GAT\n", + "\n", + "### Comparison Metabolizer Types\n", + "- **Content**: Reference metabolizer status\n", + "- **Manual Process**: Usually empty for functional studies\n", + "- **Example**: Usually empty\n", + "\n", + "### Assay Type\n", + "- **Content**: Laboratory method or experimental system used\n", + "- **Manual Process**: Extract the specific assay methodology:\n", + " - in human liver microsomes: Microsomal enzyme assays\n", + " - hydroxylation assay: Specific metabolic pathway assays\n", + " - crystal structure prediction: Computational modeling\n", + " - Leave empty if not specified\n", + "- **Example**: \n", + " - in human liver microsomes\n", + " - hydroxylation assay\n", + " - crystal structure prediction\n", + "\n", + "### Cell Type\n", + "- **Content**: Cell line or tissue system used for the assay\n", + "- **Manual Process**: Extract the specific cellular context:\n", + " - 293FT cells: Human embryonic kidney cells\n", + " - COS-7 cells: Monkey kidney cells\n", + " - Sf21 insect cells: Insect cells for baculovirus expression\n", + " - in insect microsomes: Microsomal preparations\n", + " - expressed in [cell type]: Heterologous expression systems\n", + "- **Example**: \n", + " - in 293FT cells\n", + " - expressed in COS-7 cells\n", + "\n", + "### Specialty Population\n", + "- **Content**: Age-specific populations (rarely applicable to functional studies)\n", + "- **Manual Process**: Usually leave empty for in vitro studies\n", + "- **Example**: Usually empty\n", + "\n", + "### isPlural\n", + "- **Content**: Grammar helper for sentence construction\n", + "- **Manual Process**: Use Is for singular subjects, Are for plural\n", + "- **Example**: Is\n", + "\n", + "### Is/Is Not Associated\n", + "- **Content**: Direction of functional association\n", + "- **Manual Process**: Determine association type:\n", + " - Associated with: Functional difference observed\n", + " - Not associated with: No functional difference\n", + "- **Example**: Associated with\n", + "\n", + "### Direction of Effect\n", + "- **Content**: Whether the variant increases or decreases function\n", + "- **Manual Process**: Look for directional language:\n", + " - increased: Higher activity, better function, enhanced capability\n", + " - decreased: Lower activity, reduced function, impaired capability\n", + "- **Example**: \n", + " - increased (for enhanced activity)\n", + " - decreased (for reduced activity)\n", + "\n", + "### Functional Terms\n", + "- **Content**: Specific functional outcome measured\n", + "- **Manual Process**: Extract the precise functional parameter:\n", + " - activity of: Enzyme activity measurements\n", + " - clearance of: Drug clearance kinetics\n", + " - formation of: Metabolite formation\n", + " - transport of: Transporter function\n", + " - affinity to: Binding affinity\n", + " - catalytic activity of: Catalytic efficiency\n", + "- **Example**: \n", + " - formation of\n", + " - activity of\n", + " - clearance of\n", + "\n", + "### Gene/Gene Product\n", + "- **Content**: Specific gene or protein being functionally assessed\n", + "- **Manual Process**: Extract the gene symbol when the functional term relates to gene product activity\n", + "- **Example**: CYP2C19, CYP2B6, CYP2C9\n", + "\n", + "### When Treated With/Exposed To/When Assayed With\n", + "- **Content**: Experimental substrate context\n", + "- **Manual Process**: Use standard phrases for functional assays:\n", + " - when assayed with: For enzyme activity assays\n", + " - of: For direct metabolite measurements\n", + " - Leave empty for non-substrate specific functions\n", + "- **Example**: \n", + " - when assayed with\n", + " - of\n", + "\n", + "### Multiple Drugs And/Or\n", + "- **Content**: Logical connector for multiple substrates\n", + "- **Manual Process**: If multiple substrates tested:\n", + " - and: Combination substrate assays\n", + " - or: Alternative substrate assays\n", + " - Leave empty for single substrate\n", + "- **Example**: or (for alternative substrates)\n", + "\n", + "## Manual Reading Strategy for Functional Annotations\n", + "\n", + "1. **Identify Experimental System**: Look for cell lines, microsomes, expression systems, computational models\n", + "2. **Find Functional Readouts**: Search for enzyme activity, kinetic parameters, binding affinity, transport rates\n", + "3. **Extract Substrate Information**: Identify the drug/compound used to test function\n", + "4. **Locate Comparison Data**: Find reference variants (usually wild-type or *1 alleles) for comparison\n", + "5. **Quantify Functional Changes**: Look for fold-changes, percentages, kinetic parameters (Km, Vmax, clearance)\n", + "6. **Note Experimental Conditions**: Extract assay conditions, expression systems, substrate concentrations\n", + "7. **Standardize the Relationship**: Convert findings into standardized sentence format describing the functional difference\n", + "\n", + "## Key Differences from Clinical Annotations\n", + "\n", + "- **Laboratory-based**: In vitro studies rather than patient studies\n", + "- **Mechanistic Focus**: How variants affect protein function rather than clinical outcomes\n", + "- **Quantitative Measures**: Enzyme kinetics, binding constants, activity percentages\n", + "- **Controlled Conditions**: Defined experimental systems rather than clinical populations\n", + "- **Substrate-specific**: Effects measured with specific drugs/compounds as substrates\n", + "\n", + "**Purpose**: Functional annotations provide the mechanistic basis for understanding why certain variants affect drug response in patients - they show how genetic changes alter protein function at the molecular level.\n", + "\n", + "For each term, the output should be of the format:\n", + "\n", + "Extracted Output: (output)\n", + "Reason: (one sentence justification)\n", + "Quote: (sentence from the article that demonstrates why)\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dotenv import load_dotenv\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-11 21:58:11.800\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m73\u001b[0m - \u001b[1mGetting article text from PMCID: PMC5728534\u001b[0m\n" + ] + } + ], + "source": [ + "model = SimpleLLM()\n", + "article_text = ArticleParser(pmcid=\"PMC5728534\").get_article_text()\n", + "prompt = PromptGenerator(FA_EXTRACTION_PROMPT, {\"article_text\": article_text}).get_prompt()\n", + "summary = model.generate(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "# save summary to file\n", + "with open(\"output_testing/fa_2.txt\", \"w\") as f:\n", + " f.write(summary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "default", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/update_pmcid_mapping.ipynb b/notebooks/update_pmcid_mapping.ipynb deleted file mode 100644 index 7ad2046..0000000 --- a/notebooks/update_pmcid_mapping.ipynb +++ /dev/null @@ -1,22 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pixi.lock b/pixi.lock index b38eac1..d3e417e 100644 --- a/pixi.lock +++ b/pixi.lock @@ -71,11 +71,15 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-75.1-hfee45f7_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.6.1-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.5.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-6.29.5-pyh57ce528_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.2.0-pyhfb0248b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - 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conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.15.2-py313h9a24e0a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda @@ -196,7 +206,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/statsmodels-0.14.4-py313h93df234_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tiktoken-0.9.0-py313h9a4dfeb_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h5083fa2_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tokenizers-0.21.1-py313h9a4dfeb_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.4.2-py313h90d716c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.1-pyhd8ed1ab_1.conda - 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python >=3.13,<3.14.0a0 - python >=3.13,<3.14.0a0 *_cp313 - python_abi 3.13.* *_cp313 - arch: arm64 - platform: osx license: Apache-2.0 license_family: Apache size: 150519 diff --git a/pixi.toml b/pixi.toml index 27f1b33..f505bbf 100644 --- a/pixi.toml +++ b/pixi.toml @@ -30,3 +30,4 @@ loguru = ">=0.7.2,<0.8" python-dotenv = ">=1.1.0,<2" black = ">=25.1.0,<26" datasets = ">=3.6.0,<4" +litellm = ">=1.72.2,<2" diff --git a/src/annotation_extraction/__init__.py b/src/annotation_extraction/__init__.py new file mode 100644 index 0000000..de3b94f --- /dev/null +++ b/src/annotation_extraction/__init__.py @@ -0,0 +1,38 @@ +""" +Annotation extraction module for pharmacogenomic variant annotations. + +This module implements a multi-stage LLM pipeline for extracting structured +pharmacogenomic variant annotations from biomedical articles. +""" + +from .pipeline import AnnotationPipeline +from .inference import ( + RelevanceScreener, + EntityExtractor, + AnnotationClassifier, + RowGenerator, + QualityValidator +) +from .models import ( + ArticleInput, + RelevanceResult, + ExtractedEntities, + ClassificationResult, + AnnotationRow, + ValidationResult +) + +__all__ = [ + 'AnnotationPipeline', + 'RelevanceScreener', + 'EntityExtractor', + 'AnnotationClassifier', + 'RowGenerator', + 'QualityValidator', + 'ArticleInput', + 'RelevanceResult', + 'ExtractedEntities', + 'ClassificationResult', + 'AnnotationRow', + 'ValidationResult' +] \ No newline at end of file diff --git a/src/annotation_extraction/example_usage.py b/src/annotation_extraction/example_usage.py new file mode 100644 index 0000000..88732ba --- /dev/null +++ b/src/annotation_extraction/example_usage.py @@ -0,0 +1,274 @@ +""" +Example usage of the annotation extraction pipeline. + +This script demonstrates how to use the annotation extraction system +to process biomedical articles and generate pharmacogenomic annotations. +""" + +import os +import json +from loguru import logger +from typing import List + +from .models import ArticleInput, ArticleParser +from .pipeline import AnnotationPipeline +from .stages import LLMInterface +from dotenv import load_dotenv + +load_dotenv() + + +class LiteLLMInterface(LLMInterface): + """ + LiteLLM interface implementation. + + Uses LiteLLM to support multiple LLM providers with a unified interface. + """ + + def __init__(self, model: str = "gpt-3.5-turbo", **kwargs): + """ + Initialize LiteLLM interface. + + Args: + model: Model name (e.g., "gpt-4", "claude-3-sonnet-20240229", "ollama/llama2") + **kwargs: Additional parameters for the model + """ + try: + import litellm + self.litellm = litellm + except ImportError: + raise ImportError("litellm is required. Install with: pip install litellm") + + self.model = model + self.kwargs = kwargs + + # Set API keys from environment variables + if model.startswith("gpt"): + api_key = os.getenv("OPENAI_API_KEY") + if api_key: + os.environ["OPENAI_API_KEY"] = api_key + elif model.startswith("claude"): + api_key = os.getenv("ANTHROPIC_API_KEY") + if api_key: + os.environ["ANTHROPIC_API_KEY"] = api_key + elif model.startswith("azure"): + api_key = os.getenv("AZURE_API_KEY") + if api_key: + os.environ["AZURE_API_KEY"] = api_key + + def generate(self, prompt: str, temperature: float = 0.1) -> str: + """Generate response from LLM using LiteLLM.""" + try: + response = self.litellm.completion( + model=self.model, + messages=[{"role": "user", "content": prompt}], + temperature=temperature, + **self.kwargs + ) + return response.choices[0].message.content + except Exception as e: + logger.error(f"LiteLLM generation error: {e}") + # Fallback to mock response for testing + return self._mock_response(prompt) + + def _mock_response(self, prompt: str) -> str: + """Mock responses for testing when LLM fails.""" + if "RELEVANT or NOT_RELEVANT" in prompt: + return "RELEVANT\nThis article discusses CYP2D6 variants and their effects on drug metabolism." + + elif "Extract:" in prompt and "GENETIC VARIANTS:" in prompt: + return """GENETIC VARIANTS: +- CYP2D6*4 +- rs1065852 +- CYP2C19*2/*3 + +DRUGS AND INTERVENTIONS: +- codeine +- tramadol +- omeprazole + +PHENOTYPES AND OUTCOMES: +- reduced metabolism +- poor metabolizer phenotype +- therapeutic failure + +POPULATION INFORMATION: +- European ancestry (n=150) +- Adults aged 18-65 + +ASSOCIATIONS AND RELATIONSHIPS: +- CYP2D6*4 associated with reduced codeine metabolism (p<0.001) +- Poor metabolizers show 50% reduced drug clearance""" + + elif "Classification Rules:" in prompt: + return """1 | functional, drug | high | In vitro metabolism study with clinical correlation +2 | phenotype | medium | Adverse event association in patient cohort""" + + elif "REQUIRED OUTPUT FORMAT - Tab-separated values" in prompt: + return """1451148445 CYP2D6*4 CYP2D6 codeine 12345678 Metabolism/PK yes In vitro metabolism study CYP2D6*4 is associated with reduced metabolism of codeine. *4 human liver microsomes poor metabolizer Is reduced metabolism of CYP2D6 when assayed with codeine hepatocytes *1/*1 extensive metabolizer""" + + elif "VALID or INVALID" in prompt: + return "VALID\nAll required fields are present and follow the schema." + + else: + return "Mock LLM response for: " + prompt[:100] + "..." + +def get_example_articles(num_articles: int = 2) -> List[ArticleInput]: + """Get example articles for testing.""" + # 1. Load n random markdown files from the data/articles directory + # 2. Parse the articles + # 3. Return the articles + articles = [] + for i in range(num_articles): + with open(f"data/articles/article_{i}.md", "r") as f: + articles.append(f.read()) + + return articles + + +def create_example_articles() -> List[ArticleInput]: + """Create example articles for testing.""" + + article1 = ArticleParser(pmcid="PMC16264").parse() + article2 = ArticleParser(pmcid="PMC11534822").parse() + return [article1, article2] + +def main(): + """Main example usage function.""" + + # Initialize LiteLLM interface + # Examples of different model configurations: + + # OpenAI GPT-4 + # llm = LiteLLMInterface(model="gpt-4") + + # Anthropic Claude + llm = LiteLLMInterface(model="claude-3-sonnet-20240229") + + # Local Ollama model + # llm = LiteLLMInterface(model="ollama/llama2") + + # Azure OpenAI + # llm = LiteLLMInterface(model="azure/gpt-4") + + # Default to GPT-3.5-turbo for this example + llm = LiteLLMInterface( + model="gpt-3.5-turbo", + max_tokens=4000 + ) + + # Create pipeline + pipeline = AnnotationPipeline( + llm=llm, + enable_validation=True, + parallel_processing=False # Set to True for parallel processing + ) + + # Create example articles + articles = create_example_articles() + + logger.info(f"Processing {len(articles)} example articles...") + + # Process articles + results = pipeline.process_articles(articles) + + # Display results + for result in results: + print(f"\n=== Article PMID: {result['pmid']} ===") + print(f"Success: {result['success']}") + + if result.get('relevance'): + print(f"Relevant: {result['relevance']['is_relevant']}") + if result['relevance']['is_relevant'] and result['relevance'].get('summary'): + print(f"Summary: {result['relevance']['summary']}") + + if result.get('annotations'): + print(f"Generated {len(result['annotations'])} annotations:") + for i, annotation in enumerate(result['annotations']): + print(f" {i+1}. Type: {annotation['annotation_type']}") + print(f" Fields: {len(annotation['row_data'])}") + # Show first few fields + if len(annotation['row_data']) >= 4: + print(f" Variant: {annotation['row_data'][1]}") + print(f" Gene: {annotation['row_data'][2]}") + print(f" Drug: {annotation['row_data'][3]}") + + if result.get('errors'): + print(f"Errors: {result['errors']}") + + # Get pipeline statistics + stats = pipeline.get_pipeline_stats(results) + print(f"\n=== Pipeline Statistics ===") + print(f"Total articles: {stats['total_articles']}") + print(f"Successful: {stats['successful_articles']}") + print(f"Relevant: {stats['relevant_articles']}") + print(f"Total annotations: {stats['total_annotations']}") + print(f"Annotations by type: {stats['annotations_by_type']}") + print(f"Validation pass rate: {stats['validation_pass_rate']:.2%}") + + # Example: Export to TSV files + output_dir = "example_output" + os.makedirs(output_dir, exist_ok=True) + + file_paths = pipeline.export_annotations_to_tsv(results, output_dir) + print(f"\n=== Exported Files ===") + for annotation_type, file_path in file_paths.items(): + print(f"{annotation_type}: {file_path}") + + # Example: Batch processing + print("\n=== Batch Processing Example ===") + if articles: + batch_result = pipeline.process_batch_article(articles[0]) + print(f"Batch processing success: {batch_result['success']}") + if batch_result.get('batch_annotations'): + print(f"Batch annotations: {len(batch_result['batch_annotations'])}") + + +def example_different_models(): + """ + Example showing different LLM model configurations with LiteLLM. + """ + + models_to_test = [ + { + "name": "GPT-3.5 Turbo", + "model": "gpt-3.5-turbo" + }, + { + "name": "GPT-4", + "model": "gpt-4" + }, + { + "name": "Claude 3 Sonnet", + "model": "claude-3-sonnet-20240229" + } + ] + + articles = create_example_articles()[:1] # Test with one article + + for model_config in models_to_test: + print(f"\n=== Testing {model_config['name']} ===") + + try: + llm = LiteLLMInterface( + model=model_config["model"], + max_tokens=2000 + ) + + pipeline = AnnotationPipeline(llm=llm, enable_validation=False) + results = pipeline.process_articles(articles) + + if results and results[0].get('success'): + print(f"✓ Success: {len(results[0].get('annotations', []))} annotations") + else: + print(f"✗ Failed: {results[0].get('errors', []) if results else 'No results'}") + + except Exception as e: + print(f"✗ Error: {e}") + + +if __name__ == "__main__": + main() + + # Uncomment to test different models + # example_different_models() \ No newline at end of file diff --git a/src/annotation_extraction/inference.py b/src/annotation_extraction/inference.py new file mode 100644 index 0000000..cb35484 --- /dev/null +++ b/src/annotation_extraction/inference.py @@ -0,0 +1,422 @@ +""" +Individual stages of the annotation extraction pipeline. +""" + +import re +import time +import random +from typing import List, Optional, Dict, Any +from abc import ABC, abstractmethod +from litellm import completion +from loguru import logger + +from .models import ( + ArticleInput, RelevanceResult, ExtractedEntities, ExtractedEntity, + ClassificationResult, AnnotationRow, ValidationResult, Relationship, + AnnotationType, PhenotypeCategory, Significance, Association, Direction +) +from .prompts import PromptTemplates + + +class LLMInterface(ABC): + """Abstract interface for LLM interactions.""" + + @abstractmethod + def generate(self, prompt: str, temperature: float = 0.1) -> str: + """Generate response from LLM.""" + pass + +class ModelConfig: + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): + self.model = model + self.temperature = temperature + +class RelevanceScreener: + """Stage 1: Determines if article contains relevant pharmacogenomic content.""" + + def __init__(self, llm: LLMInterface): + self.llm = llm + + def screen_article(self, article: ArticleInput) -> RelevanceResult: + """Screen article for pharmacogenomic relevance.""" + prompt = PromptTemplates.format_prompt( + PromptTemplates.RELEVANCE_SCREENING, + title=article.title, + article_text=article.article_text + ) + + response = self.llm.generate(prompt, temperature=0.1) + + # Parse response + lines = response.strip().split('\n') + first_line = lines[0].strip().upper() + + is_relevant = first_line == "RELEVANT" + summary = None + + if is_relevant and len(lines) > 1: + summary = '\n'.join(lines[1:]).strip() + + return RelevanceResult(is_relevant=is_relevant, summary=summary) + + +class EntityExtractor: + """Stage 2: Extracts pharmacogenomic entities and relationships.""" + + def __init__(self, llm: LLMInterface): + self.llm = llm + + def extract_entities(self, article: ArticleInput) -> ExtractedEntities: + """Extract all relevant entities from article.""" + prompt = PromptTemplates.format_prompt( + PromptTemplates.ENTITY_EXTRACTION, + article_text=article.article_text + ) + + response = self.llm.generate(prompt, temperature=0.2) + + # Parse the structured response + entities = self._parse_entity_response(response) + + return entities + + def _parse_entity_response(self, response: str) -> ExtractedEntities: + """Parse LLM response into structured entities.""" + sections = { + 'GENETIC VARIANTS:': [], + 'DRUGS AND INTERVENTIONS:': [], + 'PHENOTYPES AND OUTCOMES:': [], + 'POPULATION INFORMATION:': [], + 'ASSOCIATIONS AND RELATIONSHIPS:': [] + } + + current_section = None + lines = response.split('\n') + + for line in lines: + line = line.strip() + if line in sections: + current_section = line + elif current_section and line.startswith('- '): + entity_text = line[2:].strip() + entity = ExtractedEntity( + entity_type=current_section.replace(':', '').strip(), + value=entity_text, + context="", + supporting_text=entity_text + ) + sections[current_section].append(entity) + + return ExtractedEntities( + genetic_variants=sections['GENETIC VARIANTS:'], + drugs=sections['DRUGS AND INTERVENTIONS:'], + phenotypes=sections['PHENOTYPES AND OUTCOMES:'], + population_info=sections['POPULATION INFORMATION:'], + associations=sections['ASSOCIATIONS AND RELATIONSHIPS:'] + ) + + +class AnnotationClassifier: + """Stage 3: Classifies relationships into annotation types.""" + + def __init__(self, llm: LLMInterface): + self.llm = llm + + def classify_relationships(self, entities: ExtractedEntities, article: ArticleInput) -> List[ClassificationResult]: + """Classify each relationship into annotation types.""" + # Create relationships from associations + relationships = self._create_relationships(entities, article) + + if not relationships: + return [] + + # Format relationships for classification + relationships_text = self._format_relationships_for_prompt(relationships) + + prompt = PromptTemplates.format_prompt( + PromptTemplates.ANNOTATION_CLASSIFICATION, + relationships=relationships_text + ) + + response = self.llm.generate(prompt, temperature=0.1) + + # Parse classification results + return self._parse_classification_response(response, relationships) + + def _create_relationships(self, entities: ExtractedEntities, article: ArticleInput) -> List[Relationship]: + """Create relationships from extracted entities.""" + relationships = [] + + # Simple heuristic: pair variants with drugs and phenotypes + for variant in entities.genetic_variants: + # Extract gene from variant + gene = self._extract_gene_from_variant(variant.value) + + for drug in entities.drugs: + for phenotype in entities.phenotypes: + relationship = Relationship( + variant=variant.value, + gene=gene, + drug=drug.value, + phenotype=phenotype.value, + association_type="association", + evidence_text=f"{variant.supporting_text} {drug.supporting_text} {phenotype.supporting_text}", + statistical_measures=None + ) + relationships.append(relationship) + + return relationships + + def _extract_gene_from_variant(self, variant_text: str) -> str: + """Extract gene symbol from variant notation.""" + # Look for gene symbols (e.g., CYP2D6 from CYP2D6*4) + gene_pattern = r'([A-Z][A-Z0-9]+)(?:\*|\s|$)' + match = re.search(gene_pattern, variant_text) + return match.group(1) if match else "" + + def _format_relationships_for_prompt(self, relationships: List[Relationship]) -> str: + """Format relationships for classification prompt.""" + formatted = [] + for i, rel in enumerate(relationships): + formatted.append(f"{i+1}. {rel.variant} ({rel.gene}) - {rel.drug} - {rel.phenotype}") + return '\n'.join(formatted) + + def _parse_classification_response(self, response: str, relationships: List[Relationship]) -> List[ClassificationResult]: + """Parse classification response into results.""" + results = [] + lines = response.strip().split('\n') + + for line in lines: + if '|' in line: + parts = [p.strip() for p in line.split('|')] + if len(parts) >= 4: + try: + rel_id = int(parts[0]) - 1 + if 0 <= rel_id < len(relationships): + annotation_types = self._parse_annotation_types(parts[1]) + confidence = self._parse_confidence(parts[2]) + evidence = parts[3] + + result = ClassificationResult( + relationship=relationships[rel_id], + annotation_types=annotation_types, + confidence=confidence, + evidence_summary=evidence + ) + results.append(result) + except (ValueError, IndexError): + continue + + return results + + def _parse_annotation_types(self, type_text: str) -> List[AnnotationType]: + """Parse annotation types from text.""" + types = [] + type_text = type_text.lower() + + if 'functional' in type_text: + types.append(AnnotationType.FUNCTIONAL) + if 'drug' in type_text: + types.append(AnnotationType.DRUG) + if 'phenotype' in type_text: + types.append(AnnotationType.PHENOTYPE) + + return types or [AnnotationType.FUNCTIONAL] # Default to functional + + def _parse_confidence(self, confidence_text: str) -> float: + """Parse confidence level from text.""" + confidence_text = confidence_text.lower() + if 'high' in confidence_text: + return 0.9 + elif 'medium' in confidence_text: + return 0.7 + elif 'low' in confidence_text: + return 0.5 + else: + return 0.7 # Default + + +class RowGenerator: + """Stage 4: Generates schema-specific TSV rows.""" + + def __init__(self, llm: LLMInterface): + self.llm = llm + + def generate_annotation_rows(self, classifications: List[ClassificationResult], article: ArticleInput) -> List[AnnotationRow]: + """Generate annotation rows for all classifications.""" + all_rows = [] + + for classification in classifications: + for annotation_type in classification.annotation_types: + row = self._generate_single_row(classification, annotation_type, article) + if row: + all_rows.append(row) + + return all_rows + + def _generate_single_row(self, classification: ClassificationResult, + annotation_type: AnnotationType, article: ArticleInput) -> Optional[AnnotationRow]: + """Generate a single annotation row.""" + relationship = classification.relationship + + # Select appropriate prompt template + if annotation_type == AnnotationType.FUNCTIONAL: + template = PromptTemplates.FUNCTIONAL_ANNOTATION_GENERATION + elif annotation_type == AnnotationType.DRUG: + template = PromptTemplates.DRUG_ANNOTATION_GENERATION + elif annotation_type == AnnotationType.PHENOTYPE: + template = PromptTemplates.PHENOTYPE_ANNOTATION_GENERATION + else: + return None + + prompt = PromptTemplates.format_prompt( + template, + pmid=article.pmid, + relationship_description=f"{relationship.variant} - {relationship.drug} - {relationship.phenotype}", + supporting_sentence=relationship.evidence_text + ) + + response = self.llm.generate(prompt, temperature=0.1) + + # Parse the TSV row + return self._parse_tsv_response(response, annotation_type) + + def _parse_tsv_response(self, response: str, annotation_type: AnnotationType) -> Optional[AnnotationRow]: + """Parse LLM response into annotation row.""" + lines = response.strip().split('\n') + + # Find the TSV row (should be tab-separated) + for line in lines: + if '\t' in line and len(line.split('\t')) > 10: # Minimum field count + row_data = line.split('\t') + + # Generate unique ID if needed + if not row_data[0] or row_data[0] == '[Generate unique ID]': + row_data[0] = self._generate_unique_id() + + # Get field names based on annotation type + fields = self._get_field_names(annotation_type) + + return AnnotationRow( + annotation_type=annotation_type, + row_data=row_data, + fields=fields + ) + + return None + + def _generate_unique_id(self) -> str: + """Generate unique annotation ID.""" + timestamp = str(int(time.time())) + random_num = str(random.randint(1000, 9999)) + return timestamp + random_num + + def _get_field_names(self, annotation_type: AnnotationType) -> List[str]: + """Get field names for annotation type.""" + if annotation_type == AnnotationType.FUNCTIONAL: + return [ + "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", + "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", + "Specialty Population", "Assay type", "Metabolizer types", "isPlural", + "Is/Is Not associated", "Direction of effect", "Functional terms", + "Gene/gene product", "When treated with/exposed to/when assayed with", + "Multiple drugs And/or", "Cell type", "Comparison Allele(s) or Genotype(s)", + "Comparison Metabolizer types" + ] + elif annotation_type == AnnotationType.DRUG: + return [ + "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", + "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", + "Specialty Population", "Metabolizer types", "isPlural", + "Is/Is Not associated", "Direction of effect", "PD/PK terms", + "Multiple drugs And/or", "Population types", + "Population Phenotypes or diseases", "Multiple phenotypes or diseases And/or", + "Comparison Allele(s) or Genotype(s)", "Comparison Metabolizer types" + ] + elif annotation_type == AnnotationType.PHENOTYPE: + return [ + "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", + "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", + "Specialty Population", "Metabolizer types", "isPlural", + "Is/Is Not associated", "Direction of effect", "Side effect/efficacy/other", + "Phenotype", "Multiple phenotypes And/or", + "When treated with/exposed to/when assayed with", "Multiple drugs And/or", + "Population types", "Population Phenotypes or diseases", + "Multiple phenotypes or diseases And/or", + "Comparison Allele(s) or Genotype(s)", "Comparison Metabolizer types" + ] + else: + return [] + + +class QualityValidator: + """Stage 5: Validates generated annotation rows.""" + + def __init__(self, llm: LLMInterface): + self.llm = llm + + def validate_row(self, row: AnnotationRow, article: ArticleInput) -> ValidationResult: + """Validate a single annotation row.""" + # Basic validation first + basic_errors = self._basic_validation(row) + + if basic_errors: + return ValidationResult(is_valid=False, errors=basic_errors) + + # LLM-based validation + prompt = PromptTemplates.format_prompt( + PromptTemplates.QUALITY_VALIDATION, + annotation_row=row.to_tsv(), + relevant_text=article.article_text[:1000], # First 1000 chars + schema_fields=', '.join(row.fields) + ) + + response = self.llm.generate(prompt, temperature=0.1) + + return self._parse_validation_response(response, row) + + def _basic_validation(self, row: AnnotationRow) -> List[str]: + """Perform basic validation checks.""" + errors = [] + + # Check field count + expected_count = len(row.fields) + actual_count = len(row.row_data) + + if actual_count != expected_count: + errors.append(f"Field count mismatch: expected {expected_count}, got {actual_count}") + + # Check required fields are not empty + required_indices = [0, 1, 2, 4] # ID, Variant, Gene, PMID + for idx in required_indices: + if idx < len(row.row_data) and not row.row_data[idx].strip(): + errors.append(f"Required field '{row.fields[idx]}' is empty") + + # Validate controlled vocabulary fields + if len(row.row_data) > 5: # Phenotype Category + category = row.row_data[5] + valid_categories = ["Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] + if category and category not in valid_categories: + errors.append(f"Invalid Phenotype Category: {category}") + + if len(row.row_data) > 6: # Significance + significance = row.row_data[6] + valid_significance = ["yes", "no", "not stated"] + if significance and significance not in valid_significance: + errors.append(f"Invalid Significance: {significance}") + + return errors + + def _parse_validation_response(self, response: str, row: AnnotationRow) -> ValidationResult: + """Parse validation response from LLM.""" + lines = response.strip().split('\n') + + is_valid = any('VALID' in line.upper() for line in lines[:3]) + errors = [] + + # Extract error messages + for line in lines: + if line.startswith('- ') or line.startswith('* '): + errors.append(line[2:].strip()) + + return ValidationResult(is_valid=is_valid, errors=errors) \ No newline at end of file diff --git a/src/annotation_extraction/models.py b/src/annotation_extraction/models.py new file mode 100644 index 0000000..e248e0b --- /dev/null +++ b/src/annotation_extraction/models.py @@ -0,0 +1,224 @@ +""" +Data models for the annotation extraction pipeline. +""" + +from dataclasses import dataclass +from typing import List, Dict, Optional, Union +from enum import Enum +from loguru import logger +from pathlib import Path +class AnnotationType(Enum): + FUNCTIONAL = "functional" + DRUG = "drug" + PHENOTYPE = "phenotype" + + +class PhenotypeCategory(Enum): + METABOLISM_PK = "Metabolism/PK" + EFFICACY = "Efficacy" + TOXICITY = "Toxicity" + DOSAGE = "Dosage" + OTHER = "Other" + + +class Significance(Enum): + YES = "yes" + NO = "no" + NOT_STATED = "not stated" + + +class SpecialtyPopulation(Enum): + PEDIATRIC = "Pediatric" + GERIATRIC = "Geriatric" + + +class Direction(Enum): + INCREASED = "increased" + DECREASED = "decreased" + + +class Association(Enum): + IS = "Is" + IS_NOT = "Is Not" + ARE = "Are" + ARE_NOT = "Are Not" + + +@dataclass +class ArticleInput: + """Input article data.""" + title: str + article_text: str + pmid: str + pmcid: Optional[str] = None + + +class ArticleParser: + """Convert Markdown article text or PMCID to ArticleInput. + + Args: + text: Optional[str] = None + pmcid: Optional[str] = None + remove_references: bool = True + """ + def __init__(self, text: Optional[str] = None, pmcid: Optional[str] = None, remove_references: bool = True): + self.text = text + self.pmcid = pmcid + if not self.text and not self.pmcid: + logger.error("Either text or pmcid must be provided.") + raise ValueError("Either text or pmcid must be provided.") + if self.text and self.pmcid: + logger.error("Only one of text or pmcid can be provided.") + raise ValueError("Only one of text or pmcid can be provided.") + if self.pmcid: + logger.info(f"Getting article text from PMCID: {self.pmcid}") + self.text = self.get_article_text() + if self.remove_references: + self.remove_references_section() + + def get_article_text(self) -> str: + """Get article text from PMCID.""" + article_path = Path("data") / "articles" / f"{self.pmcid}.md" + if not article_path.exists(): + logger.error(f"Article not found: {article_path}") + raise FileNotFoundError(f"Article not found: {article_path}") + with open(article_path, "r", encoding="utf-8") as f: + return f.read() + + def parse_title(self) -> str: + """Parse the title from the markdown text.""" + lines = self.text.split("\n") + if not lines: + return "" + title = lines[0].strip() + if title.startswith("# "): + title = title[2:].strip() + return title + + def remove_references_section(self): + """ + Removes the references section from article text. + + Returns: + str: Article text with references section removed + (Looks for ## References section and removes it and everything after) + """ + # Split the text into sections + sections = self.text.split("##") + + # Find the index of the References section + ref_index = -1 + for i, section in enumerate(sections): + if section.strip().startswith("References"): + ref_index = i + break + + # If references section found, remove it and everything after + if ref_index != -1: + sections = sections[:ref_index] + self.text = "##".join(sections) + + logger.info(f"Removed References section from article text") + + def parse_pmid(self) -> str: + """Parse the PMID from the markdown text.""" + lines = self.text.split("\n") + + # Look for PMID in metadata section + for line in lines: + if line.strip().startswith("**PMID:**"): + pmid = line.replace("**PMID:**", "").strip() + return pmid + + return "" + + def parse_pmcid(self) -> str: + """Parse the PMCID from the markdown text.""" + if self.pmcid: + return self.pmcid + lines = self.text.split("\n") + + # Look for PMCID in metadata section + for line in lines: + if line.strip().startswith("**PMCID:**"): + pmcid = line.replace("**PMCID:**", "").strip() + return pmcid + + return "" + + def parse(self) -> ArticleInput: + """Parse the article text into an ArticleInput.""" + return ArticleInput( + title=self.parse_title(), + article_text=self.text, + pmid=self.parse_pmid(), + pmcid=self.parse_pmcid(), + ) + +@dataclass +class RelevanceResult: + """Result of relevance screening.""" + is_relevant: bool + summary: Optional[str] = None + confidence: Optional[float] = None + + +@dataclass +class ExtractedEntity: + """Single extracted entity.""" + entity_type: str + value: str + context: str + supporting_text: str + + +@dataclass +class ExtractedEntities: + """All extracted entities from an article.""" + genetic_variants: List[ExtractedEntity] + drugs: List[ExtractedEntity] + phenotypes: List[ExtractedEntity] + population_info: List[ExtractedEntity] + associations: List[ExtractedEntity] + + +@dataclass +class Relationship: + """Variant-outcome relationship.""" + variant: str + gene: str + drug: Optional[str] + phenotype: str + association_type: str + evidence_text: str + statistical_measures: Optional[Dict[str, Union[str, float]]] + + +@dataclass +class ClassificationResult: + """Result of annotation type classification.""" + relationship: Relationship + annotation_types: List[AnnotationType] + confidence: float + evidence_summary: str + + +@dataclass +class AnnotationRow: + """Generated annotation row for TSV output.""" + annotation_type: AnnotationType + row_data: List[str] + fields: List[str] + + def to_tsv(self) -> str: + """Convert to TSV format.""" + return "\t".join(self.row_data) + + +@dataclass +class ValidationResult: + """Result of quality validation.""" + is_valid: bool + errors: List[str] + corrected_row: Optional[AnnotationRow] = None + confidence_score: Optional[float] = None \ No newline at end of file diff --git a/src/annotation_extraction/pipeline.py b/src/annotation_extraction/pipeline.py new file mode 100644 index 0000000..23af21d --- /dev/null +++ b/src/annotation_extraction/pipeline.py @@ -0,0 +1,421 @@ +""" +Main pipeline orchestrator for annotation extraction. +""" + +from typing import List, Dict, Optional, Tuple +from loguru import logger +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import asdict + +from .models import ( + ArticleInput, RelevanceResult, ExtractedEntities, ClassificationResult, + AnnotationRow, ValidationResult, AnnotationType +) +from .inference import ( + LLMInterface, RelevanceScreener, EntityExtractor, AnnotationClassifier, + RowGenerator, QualityValidator +) +from .prompts import PromptTemplates + + +class AnnotationPipeline: + """Main pipeline for extracting pharmacogenomic annotations from articles.""" + + def __init__(self, llm: LLMInterface, enable_validation: bool = True, + parallel_processing: bool = False, max_workers: int = 4): + """ + Initialize the annotation pipeline. + + Args: + llm: LLM interface for generating responses + enable_validation: Whether to enable quality validation + parallel_processing: Whether to process multiple articles in parallel + max_workers: Maximum number of worker threads for parallel processing + """ + self.llm = llm + self.enable_validation = enable_validation + self.parallel_processing = parallel_processing + self.max_workers = max_workers + + # Initialize pipeline stages + self.relevance_screener = RelevanceScreener(llm) + self.entity_extractor = EntityExtractor(llm) + self.annotation_classifier = AnnotationClassifier(llm) + self.row_generator = RowGenerator(llm) + self.quality_validator = QualityValidator(llm) if enable_validation else None + + def process_article(self, article: ArticleInput) -> Dict: + """ + Process a single article through the complete pipeline. + + Args: + article: Input article data + + Returns: + Dictionary containing processing results and generated annotations + """ + logger.info(f"Processing article PMID: {article.pmid}") + + results = { + 'pmid': article.pmid, + 'relevance': None, + 'entities': None, + 'classifications': [], + 'annotations': [], + 'validation_results': [], + 'errors': [], + 'success': False + } + + try: + # Stage 1: Relevance screening + logger.debug("Stage 1: Relevance screening") + relevance = self.relevance_screener.screen_article(article) + results['relevance'] = asdict(relevance) + + if not relevance.is_relevant: + logger.info(f"Article {article.pmid} not relevant for annotation") + results['success'] = True + return results + + # Stage 2: Entity extraction + logger.debug("Stage 2: Entity extraction") + entities = self.entity_extractor.extract_entities(article) + results['entities'] = asdict(entities) + + # Stage 3: Relationship classification + logger.debug("Stage 3: Relationship classification") + classifications = self.annotation_classifier.classify_relationships(entities, article) + results['classifications'] = [asdict(c) for c in classifications] + + if not classifications: + logger.warning(f"No classifiable relationships found in article {article.pmid}") + results['success'] = True + return results + + # Stage 4: Row generation + logger.debug("Stage 4: Row generation") + annotations = self.row_generator.generate_annotation_rows(classifications, article) + results['annotations'] = [asdict(a) for a in annotations] + + # Stage 5: Quality validation (optional) + if self.enable_validation and self.quality_validator: + logger.debug("Stage 5: Quality validation") + validation_results = [] + for annotation in annotations: + validation = self.quality_validator.validate_row(annotation, article) + validation_results.append(validation) + + results['validation_results'] = [asdict(v) for v in validation_results] + + # Filter out invalid annotations if validation is enabled + valid_annotations = [] + for annotation, validation in zip(annotations, validation_results): + if validation.is_valid: + valid_annotations.append(annotation) + else: + logger.warning(f"Invalid annotation for {article.pmid}: {validation.errors}") + + results['annotations'] = [asdict(a) for a in valid_annotations] + + results['success'] = True + logger.info(f"Successfully processed article {article.pmid}: {len(results['annotations'])} annotations") + + except Exception as e: + logger.error(f"Error processing article {article.pmid}: {str(e)}") + results['errors'].append(str(e)) + results['success'] = False + + return results + + def process_articles(self, articles: List[ArticleInput]) -> List[Dict]: + """ + Process multiple articles through the pipeline. + + Args: + articles: List of input articles + + Returns: + List of processing results for each article + """ + logger.info(f"Processing {len(articles)} articles") + + if self.parallel_processing and len(articles) > 1: + return self._process_articles_parallel(articles) + else: + return self._process_articles_sequential(articles) + + def _process_articles_sequential(self, articles: List[ArticleInput]) -> List[Dict]: + """Process articles sequentially.""" + results = [] + for article in articles: + result = self.process_article(article) + results.append(result) + return results + + def _process_articles_parallel(self, articles: List[ArticleInput]) -> List[Dict]: + """Process articles in parallel.""" + results = [] + + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + # Submit all articles for processing + future_to_article = { + executor.submit(self.process_article, article): article + for article in articles + } + + # Collect results as they complete + for future in as_completed(future_to_article): + article = future_to_article[future] + try: + result = future.result() + results.append(result) + except Exception as e: + logger.error(f"Error processing article {article.pmid}: {str(e)}") + results.append({ + 'pmid': article.pmid, + 'success': False, + 'errors': [str(e)] + }) + + # Sort results by PMID to maintain consistent ordering + results.sort(key=lambda x: x.get('pmid', '')) + + return results + + def process_batch_article(self, article: ArticleInput) -> Dict: + """ + Process an article using batch processing for multiple relationships. + + This method uses the batch processing prompt to handle articles + with many variant-outcome relationships more efficiently. + + Args: + article: Input article data + + Returns: + Dictionary containing processing results + """ + logger.info(f"Batch processing article PMID: {article.pmid}") + + results = { + 'pmid': article.pmid, + 'relevance': None, + 'batch_annotations': [], + 'validation_results': [], + 'errors': [], + 'success': False + } + + try: + # Stage 1: Relevance screening + relevance = self.relevance_screener.screen_article(article) + results['relevance'] = asdict(relevance) + + if not relevance.is_relevant: + logger.info(f"Article {article.pmid} not relevant for batch annotation") + results['success'] = True + return results + + # Batch processing prompt + prompt = PromptTemplates.format_prompt( + PromptTemplates.BATCH_PROCESSING, + pmid=article.pmid, + full_article=article.article_text + ) + + response = self.llm.generate(prompt, temperature=0.2) + + # Parse batch response + annotations = self._parse_batch_response(response) + results['batch_annotations'] = [asdict(a) for a in annotations] + + # Optional validation + if self.enable_validation and self.quality_validator: + validation_results = [] + for annotation in annotations: + validation = self.quality_validator.validate_row(annotation, article) + validation_results.append(validation) + + results['validation_results'] = [asdict(v) for v in validation_results] + + # Filter valid annotations + valid_annotations = [] + for annotation, validation in zip(annotations, validation_results): + if validation.is_valid: + valid_annotations.append(annotation) + + results['batch_annotations'] = [asdict(a) for a in valid_annotations] + + results['success'] = True + logger.info(f"Batch processed article {article.pmid}: {len(results['batch_annotations'])} annotations") + + except Exception as e: + logger.error(f"Error batch processing article {article.pmid}: {str(e)}") + results['errors'].append(str(e)) + results['success'] = False + + return results + + def _parse_batch_response(self, response: str) -> List[AnnotationRow]: + """Parse batch processing response into annotation rows.""" + annotations = [] + lines = response.strip().split('\n') + + current_type = None + + for line in lines: + line = line.strip() + + if line.startswith('ANNOTATION_TYPE:'): + type_text = line.split(':', 1)[1].strip().lower() + if 'functional' in type_text: + current_type = AnnotationType.FUNCTIONAL + elif 'drug' in type_text: + current_type = AnnotationType.DRUG + elif 'phenotype' in type_text: + current_type = AnnotationType.PHENOTYPE + + elif line.startswith('ROW:') and current_type: + row_data = line.split(':', 1)[1].strip() + if '\t' in row_data: + fields = self._get_field_names_for_type(current_type) + annotation = AnnotationRow( + annotation_type=current_type, + row_data=row_data.split('\t'), + fields=fields + ) + annotations.append(annotation) + + return annotations + + def _get_field_names_for_type(self, annotation_type: AnnotationType) -> List[str]: + """Get field names for annotation type.""" + if annotation_type == AnnotationType.FUNCTIONAL: + return [ + "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", + "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", + "Specialty Population", "Assay type", "Metabolizer types", "isPlural", + "Is/Is Not associated", "Direction of effect", "Functional terms", + "Gene/gene product", "When treated with/exposed to/when assayed with", + "Multiple drugs And/or", "Cell type", "Comparison Allele(s) or Genotype(s)", + "Comparison Metabolizer types" + ] + elif annotation_type == AnnotationType.DRUG: + return [ + "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", + "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", + "Specialty Population", "Metabolizer types", "isPlural", + "Is/Is Not associated", "Direction of effect", "PD/PK terms", + "Multiple drugs And/or", "Population types", + "Population Phenotypes or diseases", "Multiple phenotypes or diseases And/or", + "Comparison Allele(s) or Genotype(s)", "Comparison Metabolizer types" + ] + elif annotation_type == AnnotationType.PHENOTYPE: + return [ + "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", + "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", + "Specialty Population", "Metabolizer types", "isPlural", + "Is/Is Not associated", "Direction of effect", "Side effect/efficacy/other", + "Phenotype", "Multiple phenotypes And/or", + "When treated with/exposed to/when assayed with", "Multiple drugs And/or", + "Population types", "Population Phenotypes or diseases", + "Multiple phenotypes or diseases And/or", + "Comparison Allele(s) or Genotype(s)", "Comparison Metabolizer types" + ] + else: + return [] + + def export_annotations_to_tsv(self, results: List[Dict], output_dir: str) -> Dict[str, str]: + """ + Export annotations to TSV files by type. + + Args: + results: List of processing results + output_dir: Directory to save TSV files + + Returns: + Dictionary mapping annotation types to file paths + """ + import os + from collections import defaultdict + + # Group annotations by type + annotations_by_type = defaultdict(list) + + for result in results: + if result.get('success') and result.get('annotations'): + for annotation_dict in result['annotations']: + annotation_type = annotation_dict['annotation_type'] + annotations_by_type[annotation_type].append(annotation_dict) + + # Write TSV files + file_paths = {} + + for annotation_type, annotations in annotations_by_type.items(): + if not annotations: + continue + + filename = f"var_{annotation_type}_ann.tsv" + file_path = os.path.join(output_dir, filename) + + with open(file_path, 'w', encoding='utf-8') as f: + # Write header + if annotations: + header = '\t'.join(annotations[0]['fields']) + f.write(header + '\n') + + # Write data rows + for annotation in annotations: + row = '\t'.join(annotation['row_data']) + f.write(row + '\n') + + file_paths[annotation_type] = file_path + logger.info(f"Exported {len(annotations)} {annotation_type} annotations to {file_path}") + + return file_paths + + def get_pipeline_stats(self, results: List[Dict]) -> Dict: + """Get statistics about pipeline processing.""" + stats = { + 'total_articles': len(results), + 'successful_articles': 0, + 'relevant_articles': 0, + 'total_annotations': 0, + 'annotations_by_type': {}, + 'validation_pass_rate': 0.0, + 'error_count': 0, + 'errors': [] + } + + for result in results: + if result.get('success'): + stats['successful_articles'] += 1 + + if result.get('relevance', {}).get('is_relevant'): + stats['relevant_articles'] += 1 + + # Count annotations + if result.get('annotations'): + stats['total_annotations'] += len(result['annotations']) + + for annotation in result['annotations']: + ann_type = annotation.get('annotation_type', 'unknown') + stats['annotations_by_type'][ann_type] = stats['annotations_by_type'].get(ann_type, 0) + 1 + + # Validation statistics + if result.get('validation_results'): + valid_count = sum(1 for v in result['validation_results'] if v.get('is_valid')) + total_count = len(result['validation_results']) + if total_count > 0: + stats['validation_pass_rate'] += valid_count / total_count + + if result.get('errors'): + stats['error_count'] += len(result['errors']) + stats['errors'].extend(result['errors']) + + # Average validation pass rate + if stats['successful_articles'] > 0: + stats['validation_pass_rate'] /= stats['successful_articles'] + + return stats \ No newline at end of file diff --git a/src/annotation_extraction/prompts.py b/src/annotation_extraction/prompts.py new file mode 100644 index 0000000..1885036 --- /dev/null +++ b/src/annotation_extraction/prompts.py @@ -0,0 +1,291 @@ +""" +Prompt templates for the LLM annotation pipeline. +""" + +from typing import Dict, Any + + +class PromptTemplates: + """Container for all prompt templates used in the pipeline.""" + + VAR_DRUG_SUMMARY = """ + +You are extracting pharmacogenomic information from a biomedical article. Identify and extract the following entities and their relationships: + +Article: {article_text} + +Extract: +METHODS SECTION: +- Briefly summarize the methods and results of the study + +GENETIC VARIANTS (if present): +- SNP IDs (rs numbers) + - For each SNP, capture: + - Specific gene location + - Variant type (missense, synonymous, etc.) + - Potential functional impact on protein + - Observed genotype frequencies + - Explicitly link each genetic variant to: + - Specific drug response + - Changes in clinical outcomes + - Mechanism of pharmacological interaction +- Gene names and symbols +- Variant/haplotype names (e.g., CYP2D6*4, *1/*2) +- Allele designations +- Genotype combinations +- If/how the variants affected the outcome of the study + +DRUGS AND INTERVENTIONS: +- Drug names (generic and brand) +- Drug classes and categories +- Dosing information +- Treatment regimens + +PHENOTYPES AND OUTCOMES: +- Clinical phenotypes +- Adverse events and toxicities +- Efficacy measures +- Pharmacokinetic parameters (clearance, metabolism, etc.) +- Disease conditions +- If/how the phenotypes affected the outcome of the study + +POPULATION INFORMATION: +- Study population characteristics +- Demographics (age, ethnicity, gender) +- Sample sizes +- Inclusion/exclusion criteria + +ASSOCIATIONS AND RELATIONSHIPS: +- Statistical associations between variants and outcomes +- Odds ratios, p-values, confidence intervals +- Comparative statements (increased/decreased effects) +- Causal relationships + +Format your response as structured lists with clear entity types and associated context. + +""" + + RELEVANCE_SCREENING = """ +You are a pharmacogenomics expert reviewing biomedical literature. Analyze this article and determine if it contains information about genetic variants and their associations with drug response, metabolism, toxicity, or disease phenotypes. + +Article Title: {title} +Article Text: {article_text} + +Does this article contain: +1. Specific genetic variants (SNPs, haplotypes, alleles)? +2. Drug names or therapeutic interventions? +3. Clinical outcomes, drug responses, or phenotypic associations? +4. Population or patient cohort data? + +Respond with: RELEVANT or NOT_RELEVANT +If RELEVANT, provide a brief 2-3 sentence summary of the key pharmacogenomic findings.""" + + ENTITY_EXTRACTION = """ +You are extracting pharmacogenomic information from a biomedical article. Identify and extract the following entities and their relationships: + +Article: {article_text} + +Extract: + +GENETIC VARIANTS: +- SNP IDs (rs numbers) +- Gene names and symbols +- Variant/haplotype names (e.g., CYP2D6*4, *1/*2) +- Allele designations +- Genotype combinations + +DRUGS AND INTERVENTIONS: +- Drug names (generic and brand) +- Drug classes and categories +- Dosing information +- Treatment regimens + +PHENOTYPES AND OUTCOMES: +- Clinical phenotypes +- Adverse events and toxicities +- Efficacy measures +- Pharmacokinetic parameters (clearance, metabolism, etc.) +- Disease conditions + +POPULATION INFORMATION: +- Study population characteristics +- Demographics (age, ethnicity, gender) +- Sample sizes +- Inclusion/exclusion criteria + +ASSOCIATIONS AND RELATIONSHIPS: +- Statistical associations between variants and outcomes +- Odds ratios, p-values, confidence intervals +- Comparative statements (increased/decreased effects) +- Causal relationships + +Format your response as structured lists with clear entity types and associated context.""" + + ANNOTATION_CLASSIFICATION = """ +Based on the extracted entities and relationships, classify each variant-outcome association into annotation categories: + +Extracted Relationships: {relationships} + +Classification Rules: +- FUNCTIONAL ANNOTATION: In vitro studies, enzyme activity, protein expression, cellular assays, metabolism studies +- DRUG ANNOTATION: Clinical drug response, efficacy, dosing, therapeutic outcomes in patients +- PHENOTYPE ANNOTATION: Adverse events, toxicity, disease susceptibility, clinical phenotypes + +For each relationship, specify: +1. Annotation type(s) applicable +2. Confidence level (high/medium/low) +3. Key evidence supporting the classification + +Output format: +Relationship ID | Annotation Type(s) | Confidence | Evidence Summary""" + + FUNCTIONAL_ANNOTATION_GENERATION = """Generate a complete functional annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. + +Article PMID: {pmid} +Relationship: {relationship_description} +Relevant Text: {supporting_sentence} + +REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: + +Variant Annotation ID: [Generate unique ID: concatenate timestamp + random numbers] +Variant/Haplotypes: [Exact variant notation, e.g., "CYP2C19*1, CYP2C19*2" or "rs1234567"] +Gene: [Gene symbol only, e.g., "CYP2C19"] +Drug(s): [Generic drug name(s), e.g., "warfarin" or "clopidogrel, aspirin"] +PMID: [{pmid}] +Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] +Significance: [EXACTLY ONE: "yes", "no", "not stated"] +Notes: [Brief context or methodology, e.g., "in vitro expression study"] +Sentence: [Complete supporting sentence from article that describes the association] +Alleles: [Specific allele if different from Variant/Haplotypes, or empty] +Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or leave empty] +Assay type: [Laboratory method, e.g., "yeast microsomes", "human liver microsomes"] +Metabolizer types: [If applicable: "poor metabolizer", "intermediate metabolizer", etc.] +isPlural: [Leave empty - automatically determined] +Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] +Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty if no direction] +Functional terms: [Specific functional outcome, e.g., "activity of", "metabolism of", "clearance of"] +Gene/gene product: [Gene symbol if functional term relates to gene product] +When treated with/exposed to/when assayed with: [Drug context if applicable] +Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] +Cell type: [If in vitro study, specify cell type] +Comparison Allele(s) or Genotype(s): [Reference genotype for comparison] +Comparison Metabolizer types: [Reference metabolizer status if applicable] + +OUTPUT EXACTLY ONE TAB-SEPARATED ROW with all fields filled or empty as appropriate.""" + + DRUG_ANNOTATION_GENERATION = """Generate a complete drug annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. + +Article PMID: {pmid} +Relationship: {relationship_description} +Relevant Text: {supporting_sentence} + +REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: + +Variant Annotation ID: [Generate unique ID] +Variant/Haplotypes: [Exact variant notation] +Gene: [Gene symbol only] +Drug(s): [Generic drug name(s)] +PMID: [{pmid}] +Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] +Significance: [EXACTLY ONE: "yes", "no", "not stated"] +Notes: [Clinical context or study details] +Sentence: [Complete supporting sentence from article] +Alleles: [Specific allele if different from Variant/Haplotypes, or empty] +Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or empty] +Metabolizer types: [If applicable: metabolizer status] +isPlural: [Leave empty] +Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] +Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty] +PD/PK terms: [Pharmacokinetic/pharmacodynamic outcome, e.g., "concentrations of", "response to"] +Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] +Population types: [Ethnicity/ancestry if specified] +Population Phenotypes or diseases: [Disease with prefix: "Disease:", "Other:", "Side Effect:"] +Multiple phenotypes or diseases And/or: [EXACTLY ONE: "and", "or", or empty] +Comparison Allele(s) or Genotype(s): [Reference genotype] +Comparison Metabolizer types: [Reference metabolizer status] + +OUTPUT EXACTLY ONE TAB-SEPARATED ROW.""" + + PHENOTYPE_ANNOTATION_GENERATION = """Generate a complete phenotype annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. + +Article PMID: {pmid} +Relationship: {relationship_description} +Relevant Text: {supporting_sentence} + +REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: + +Variant Annotation ID: [Generate unique ID] +Variant/Haplotypes: [Exact variant notation] +Gene: [Gene symbol only] +Drug(s): [Generic drug name(s)] +PMID: [{pmid}] +Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] +Significance: [EXACTLY ONE: "yes", "no", "not stated"] +Notes: [Clinical context] +Sentence: [Complete supporting sentence from article] +Alleles: [Specific allele if different, or empty] +Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or empty] +Metabolizer types: [If applicable] +isPlural: [Leave empty] +Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] +Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty] +Side effect/efficacy/other: [Specific phenotype outcome with prefix: "Side Effect:", "Efficacy:", "Other:"] +Phenotype: [Primary phenotype, e.g., "Neutropenia", "Stevens-Johnson Syndrome"] +Multiple phenotypes And/or: [EXACTLY ONE: "and", "or", or empty] +When treated with/exposed to/when assayed with: [Drug context] +Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] +Population types: [Ethnicity if specified] +Population Phenotypes or diseases: [Disease context with prefix] +Multiple phenotypes or diseases And/or: [EXACTLY ONE: "and", "or", or empty] +Comparison Allele(s) or Genotype(s): [Reference genotype] +Comparison Metabolizer types: [Reference metabolizer status] + +OUTPUT EXACTLY ONE TAB-SEPARATED ROW.""" + + QUALITY_VALIDATION = """Review this generated annotation row and validate it meets the schema requirements: + +Generated Row: {annotation_row} +Source Article Excerpt: {relevant_text} +Expected Schema: {schema_fields} + +Validation Checklist: +1. Are ALL required fields present in correct order? +2. Do controlled vocabulary fields use exact allowed values? +3. Is the variant notation standardized? +4. Are drug names generic/standard? +5. Does the sentence directly support the association? +6. Are comparison groups appropriate? +7. Is the statistical significance correctly interpreted? + +Output: +- VALID or INVALID +- List specific corrections needed +- Corrected row if invalid""" + + BATCH_PROCESSING = """ +This article contains multiple pharmacogenomic relationships. Generate separate annotation rows for each relationship. + +Article PMID: {pmid} +Article Text: {full_article} + +For each distinct variant-drug-outcome relationship found: +1. Determine annotation type (functional/drug/phenotype) +2. Generate complete TSV row following the appropriate schema +3. Ensure unique Variant Annotation IDs +4. Maintain consistency in variant nomenclature across rows + +Output format: +ANNOTATION_TYPE: [functional/drug/phenotype] +ROW: [complete tab-separated row] + +ANNOTATION_TYPE: [functional/drug/phenotype] +ROW: [complete tab-separated row] + +[Continue for all relationships found] + +Ensure no duplicate relationships and complete coverage of annotatable findings.""" + + @classmethod + def format_prompt(cls, template: str, **kwargs) -> str: + """Format a prompt template with provided parameters.""" + return template.format(**kwargs) \ No newline at end of file diff --git a/src/annotation_extraction/simple_inference.py b/src/annotation_extraction/simple_inference.py index 4013ef2..8418de5 100644 --- a/src/annotation_extraction/simple_inference.py +++ b/src/annotation_extraction/simple_inference.py @@ -26,36 +26,6 @@ def validate_replacements(self) -> bool: def generate_prompt(self) -> str: return self.prompt_template.format(**self.replacements) - def remove_references(self) -> str: - """Remove references from the prompt.""" - return re.sub(r'\[[0-9]+\]', '', self.prompt) - - def remove_references_section(self) -> str: - """ - Removes the references section from article text. - - Returns: - str: Article text with references section removed - (Looks for ## References section and removes it and everything after) - """ - # Split the text into sections - sections = self.prompt.split("##") - - # Find the index of the References section - ref_index = -1 - for i, section in enumerate(sections): - if section.strip().startswith("References"): - ref_index = i - break - - # If references section found, remove it and everything after - if ref_index != -1: - sections = sections[:ref_index] - return "##".join(sections) - - return self.prompt - - def get_prompt(self) -> str: return self.prompt diff --git a/src/annotation_extraction/simple_prompts.py b/src/annotation_extraction/simple_prompts.py new file mode 100644 index 0000000..7845789 --- /dev/null +++ b/src/annotation_extraction/simple_prompts.py @@ -0,0 +1,9 @@ +class SimplePrompts: + """Simple prompts for the LLM annotation pipeline.""" + + SUMMARIZE_PROMPT = """ +You are a pharmacogenomics expert reviewing biomedical literature. Analyze this article and determine if it contains information about genetic variants and their associations with drug response, metabolism, toxicity, or disease phenotypes. + +Article Title: {title} +Article Text: {article_text} +""" diff --git a/src/annotation_extraction/testing.ipynb b/src/annotation_extraction/testing.ipynb new file mode 100644 index 0000000..e69de29 From a0334f1864a3607117101e9e4204c3b7a015d05c Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Fri, 13 Jun 2025 14:12:06 -0700 Subject: [PATCH 07/28] feat: extract variant list component --- docs/prompts/list_all_variants.txt | 6 + docs/prompts/summarize_drug.txt | 167 +++++ docs/prompts/triage.txt | 44 ++ docs/prompts/variant_list_old.txt | 10 + notebooks/test.ipynb | 703 ++++-------------- src/annotation_extraction/article_parser.py | 114 +++ src/annotation_extraction/components.py | 43 ++ src/annotation_extraction/models.py | 113 --- src/annotation_extraction/simple_inference.py | 80 +- src/annotation_extraction/utils.py | 7 + 10 files changed, 622 insertions(+), 665 deletions(-) create mode 100644 docs/prompts/list_all_variants.txt create mode 100644 docs/prompts/summarize_drug.txt create mode 100644 docs/prompts/triage.txt create mode 100644 docs/prompts/variant_list_old.txt create mode 100644 src/annotation_extraction/article_parser.py create mode 100644 src/annotation_extraction/components.py create mode 100644 src/annotation_extraction/utils.py diff --git a/docs/prompts/list_all_variants.txt b/docs/prompts/list_all_variants.txt new file mode 100644 index 0000000..4dd69a6 --- /dev/null +++ b/docs/prompts/list_all_variants.txt @@ -0,0 +1,6 @@ +You are an expert pharmacogenomics researcher reading and extracting annotations from the following article: + +{article_text} + +From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.) that are part of the article's +study/analysis. Your output format should be a list of strings \ No newline at end of file diff --git a/docs/prompts/summarize_drug.txt b/docs/prompts/summarize_drug.txt new file mode 100644 index 0000000..072692f --- /dev/null +++ b/docs/prompts/summarize_drug.txt @@ -0,0 +1,167 @@ +You are an expert pharmacogenomics researcher reading and extracting annotations from the following article + +\n\n{article_text}\n\n + +From this article: +- Briefly summarize the methods and results of the study +- Create a list of extract all variants that have a studied described effect on drug response. +Make sure for each variant <-> drug relationship in the list +1. The (variant, drug(s)) pair +2. The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18 ) +3. The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11) +4. Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del) +5. Describe the drug(s) in this relationship +6. Describe the outcome/affect found from the variant (drug efficacy, metabolism, toxicity, dosage, etc.) +7. Describe the statistical analysis used and the reported p-values +8. Describe the population of the study participants for this variant +9. Describe any other useful information included on this variant to understanding the study results. + + +Term: Variant/Haplotypes +- Content: The specific genetic variant mentioned in the study +- Manual Process: Look for SNP IDs (rs numbers), star alleles (CYP2D6*4), or genotype combinations +- Example: rs2909451, CYP2C19*1, CYP2C19*2, *1/*18 + +Term: Gene +- Content: Gene symbol associated with the variant +- Manual Process: Find the gene name near the variant mention, use standard HUGO symbols +- Example: DPP4, CYP2C19, KCNJ11 + +Term: Drug(s) +- Content: Generic drug name(s) studied +- Manual Process: Extract drug names from methods/results, use generic names, separate multiple drugs with commas +- Example: sitagliptin, clopidogrel, aspirin + +Term: Phenotype Category +- Content: Type of clinical outcome studied +- Manual Process: Categorize based on what was measured: + - Efficacy: Treatment response, clinical improvement + - Metabolism/PK: Drug levels, clearance, half-life + - Toxicity: Adverse events, side effects + - Dosage: Dose requirements, dose adjustments + - Other: Everything else +- Example: Efficacy (for HbA1c improvement study) + +Term: Significance +- Content: Whether the association was statistically significant +- Manual Process: Look for p-values, confidence intervals: + - yes: p < 0.05 or explicitly stated as significant + - no: p ≥ 0.05 or stated as non-significant + - not stated: No statistical testing mentioned +- Example: yes (P < .001 in sitagliptin study) + +Term: Notes +- Content: Key study details, methodology, or important context +- Manual Process: Extract relevant quotes showing statistical results, study design, or important caveats +- Example: "Patients with the rs2909451 TT genotype in the study group exhibited a median HbA1c improvement of 0.57..." + +Term: Standardized Sentence + +- Content: Standardized description of the genetic association +- Manual Process: Write in format: "[Genotype/Allele] is [associated with/not associated with] [increased/decreased] +[outcome] [drug context] [population context]" +- Example: "Genotype TT is associated with decreased response to sitagliptin in people with Diabetes Mellitus, Type 2." + +Term: Alleles + +- Content: Specific allele or genotype if different from Variant/Haplotypes field +- Manual Process: Extract the exact genotype mentioned (AA, TT, CC, del/del, etc.) +- Example: TT, *1/*18, del/del + +Term: Metabolizer types + +- Content: CYP enzyme phenotype categories +- Manual Process: Look for metabolizer classifications in CYP studies: + - poor metabolizer, intermediate metabolizer, extensive metabolizer, ultrarapid metabolizer +- Example: intermediate metabolizer + +Term: Comparison Allele(s) or Genotype(s) + +- Content: Reference genotype used for comparison +- Manual Process: Find what the study variant was compared against +- Example: *1/*1, C (for wild-type comparisons) + +Term: Comparison Metabolizer types + +- Content: Reference metabolizer status for comparison +- Manual Process: Extract the comparison metabolizer phenotype +- Example: normal metabolizer + +Term: Specialty Population + +- Content: Age-specific populations +- Manual Process: Check if study specifically focused on: + - Pediatric: Children/adolescents + - Geriatric: Elderly patients + - Leave empty for general adult populations + +Term: Population types +- Content: Descriptor of study population +- Manual Process: Look for population descriptors, usually "in people with" or ethnicity information +- Example: in people with + +Term: Population Phenotypes or diseases +- Content: Disease/condition context with standardized prefix +- Manual Process: Find the medical condition studied, add appropriate prefix: + - Disease: for established diseases + - Other: for conditions/traits + - Side Effect: for adverse events +- Example: Other:Diabetes Mellitus, Type 2 + +Term: isPlural +- Content: Grammar helper for sentence construction +- Manual Process: Use Is for singular subjects, Are for plural +- Example: Is + +Term: Is/Is Not associated +- Content: Direction of association +- Manual Process: Determine if association was: + - Associated with: Positive association found + - Not associated with: No association found +- Example: Associated with + +Term: Direction of effect + +- Content: Whether the effect increases or decreases the outcome +- Manual Process: Look for directional language: + - increased: Higher levels, better response, more effect + - decreased: Lower levels, worse response, less effect + - Leave empty if no clear direction +- Example: decreased + +Term: PD/PK terms + +- Content: Pharmacological outcome descriptor +- Manual Process: Extract the specific outcome measured: + - response to, concentrations of, metabolism of, clearance of, dose of +- Example: response to + +Term: Multiple drugs And/or + +- Content: Logical connector for multiple drugs +- Manual Process: If multiple drugs mentioned: + - and: All drugs together + - or: Any of the drugs + - Leave empty for single drug + +Term: Multiple phenotypes or diseases And/or + +- Content: Logical connector for multiple conditions +- Manual Process: Similar to drugs, use and/or for multiple conditions +- Leave empty for single condition + +General recommended strategies + +1. Scan for genetic variants: Look for "rs" numbers, gene names with asterisks, or phrases like "genotype," "allele," +"polymorphism" +2. Identify drug context: Find drug names in methods, results, or discussion sections +3. Locate outcome measures: Look for clinical endpoints, lab values, response rates, adverse events +4. Find statistical associations: Search for p-values, odds ratios, significant differences between genotype groups +5. Extract population details: Note the study population, disease context, and inclusion criteria +6. Standardize the relationship: Convert the finding into the standardized sentence format following the association pattern + +For each term, the output should be of the format: + +Extracted Output: (output) +Reason: (one sentence justification) +Quote: (quote from the article that demonstrates why) \ No newline at end of file diff --git a/docs/prompts/triage.txt b/docs/prompts/triage.txt new file mode 100644 index 0000000..78bb259 --- /dev/null +++ b/docs/prompts/triage.txt @@ -0,0 +1,44 @@ +You are determining what type of annotations are appropritate for the article from the options of to extract from a biomedical article. For the article determine the following: + +Article: \n\n{article_text}\n\n + +Articles get Variant Drug Annotations when they report associations between genetic variants and +pharmacological parameters or clinical drug response measures that specifically relate to: +- Pharmacokinetic/Pharmacodynamic Parameters +- Clinical phenotypes/adverse events (Drug toxicity, organ dysfunction, treatment response phenotypes, disease outcomes when treated with drugs) + +Articles get Variant Phenotype Annotations when they report associations between genetic variants and adverse drug reactions, toxicities, or clinical outcomes that represent: +- Toxicity/Safety outcomes +- Clinical phenotypes/adverse events + +Articles get Variant Functional Annotations when they contain in vitro or mechanistic functional studies that directly measure how genetic variants affect: + +- Enzyme/transporter activity (e.g., clearance, metabolism, transport) +- Binding affinity (e.g., protein-drug interactions) +- Functional properties (e.g., uptake rates, kinetic parameters like Km/Vmax) + +The key distinction is mechanistic functional studies (gets var_fa_ann) vs clinical association studies (gets var_drug_ann or var_pheno_ann but not var_fa_ann). + +Examples: + +- "Cardiotoxicity when treated with anthracyclines" → var_pheno_ann +- "Decreased clearance of methotrexate" → var_drug_ann +- "Decreased enzyme activity in cell culture" → var_fa_ann +- "Variant affects drug clearance/response" —> var_drug_ann +- "Variant affects adverse events/toxicity outcomes" —> var_pheno_ann +- "Variant affects protein function in laboratory studies" —> var_fa_ann + +Using this information, decide which out of the 3 annotations the article should receive with a one sentence summary reason along with a sentence/quote from the article that indicates why this is true. + +Output Format: +Variant Drug Annotation: (Y/N) +Reason: (Reason) +Quote:(Quote) + +Variant Phenotype Annotation: (Y/N) +Reason: (Reason) +Quote:(Quote) + +Variant Functional Annotation: (Y/N) +Reason: (Reason) +Quote:(Quote) \ No newline at end of file diff --git a/docs/prompts/variant_list_old.txt b/docs/prompts/variant_list_old.txt new file mode 100644 index 0000000..b214e4d --- /dev/null +++ b/docs/prompts/variant_list_old.txt @@ -0,0 +1,10 @@ +You are an expert pharmacogenomics researcher reading and extracting annotations from the following article + +\n\n{article_text}\n\n + +From this article, extract a list of all variants described in the paper along with their gene group and allele (if present). +For each variant, output the following: +Variant: The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18, etc.) +Gene: The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11, etc.) +Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.) +Sentence: The sentence from the article that contains the mention of the variant. This should be a quote from the article, not something you inferred. \ No newline at end of file diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb index 2f91d52..5f4ed59 100644 --- a/notebooks/test.ipynb +++ b/notebooks/test.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# Run this cell: \n", "# The lines below will instruct jupyter to reload imported modules before \n", @@ -18,25 +27,49 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB\n" + ] + } + ], + "source": [ + "import os\n", + "# Change path to project root\n", + "if os.getcwd().endswith(\"notebooks\"):\n", + " os.chdir(os.path.dirname(os.getcwd()))\n", + "print(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ "from src.annotation_extraction.simple_inference import SimpleLLM, PromptGenerator\n", - "from src.annotation_extraction.models import ArticleParser" + "from src.annotation_extraction.models import ArticleParser\n", + "from typing import List" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 125, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-11 15:12:58.134\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m73\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-11 15:12:58.137\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m73\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n" + "\u001b[32m2025-06-13 13:40:33.257\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m13\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-13 13:40:33.261\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m121\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-13 13:40:33.262\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m13\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-13 13:40:33.264\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m121\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" ] } ], @@ -47,600 +80,180 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 126, "metadata": {}, "outputs": [], "source": [ - "DRUG_EXTRACTION_PROMPT = \"\"\"\n", + "DRUG_SUMMARY = \"\"\"\n", "You are an expert pharmacogenomics researcher reading and extracting annotations from the following article\n", "\n", "\\n\\n{article_text}\\n\\n\n", "\n", - "These are the following terms for which we need to extract values:\n", - "\n", - "Term: Variant/Haplotypes\n", - "- Content: The specific genetic variant mentioned in the study\n", - "- Manual Process: Look for SNP IDs (rs numbers), star alleles (CYP2D6*4), or genotype combinations\n", - "- Example: rs2909451, CYP2C19*1, CYP2C19*2, *1/*18\n", - "\n", - "Term: Gene\n", - "- Content: Gene symbol associated with the variant\n", - "- Manual Process: Find the gene name near the variant mention, use standard HUGO symbols\n", - "- Example: DPP4, CYP2C19, KCNJ11\n", - "\n", - "Term: Drug(s)\n", - "- Content: Generic drug name(s) studied\n", - "- Manual Process: Extract drug names from methods/results, use generic names, separate multiple drugs with commas\n", - "- Example: sitagliptin, clopidogrel, aspirin\n", - "\n", - "Term: Phenotype Category\n", - "- Content: Type of clinical outcome studied\n", - "- Manual Process: Categorize based on what was measured:\n", - " - Efficacy: Treatment response, clinical improvement\n", - " - Metabolism/PK: Drug levels, clearance, half-life\n", - " - Toxicity: Adverse events, side effects\n", - " - Dosage: Dose requirements, dose adjustments\n", - " - Other: Everything else\n", - "- Example: Efficacy (for HbA1c improvement study)\n", - "\n", - "Term: Significance\n", - "- Content: Whether the association was statistically significant\n", - "- Manual Process: Look for p-values, confidence intervals:\n", - " - yes: p < 0.05 or explicitly stated as significant\n", - " - no: p ≥ 0.05 or stated as non-significant\n", - " - not stated: No statistical testing mentioned\n", - "- Example: yes (P < .001 in sitagliptin study)\n", - "\n", - "Term: Notes\n", - "- Content: Key study details, methodology, or important context\n", - "- Manual Process: Extract relevant quotes showing statistical results, study design, or important caveats\n", - "- Example: \"Patients with the rs2909451 TT genotype in the study group exhibited a median HbA1c improvement of 0.57...\"\n", - "\n", - "Term: Standardized Sentence\n", - "\n", - "- Content: Standardized description of the genetic association\n", - "- Manual Process: Write in format: \"[Genotype/Allele] is [associated with/not associated with] [increased/decreased]\n", - "[outcome] [drug context] [population context]\"\n", - "- Example: \"Genotype TT is associated with decreased response to sitagliptin in people with Diabetes Mellitus, Type 2.\"\n", - "\n", - "Term: Alleles\n", - "\n", - "- Content: Specific allele or genotype if different from Variant/Haplotypes field\n", - "- Manual Process: Extract the exact genotype mentioned (AA, TT, CC, del/del, etc.)\n", - "- Example: TT, *1/*18, del/del\n", - "\n", - "Term: Metabolizer types\n", - "\n", - "- Content: CYP enzyme phenotype categories\n", - "- Manual Process: Look for metabolizer classifications in CYP studies:\n", - " - poor metabolizer, intermediate metabolizer, extensive metabolizer, ultrarapid metabolizer\n", - "- Example: intermediate metabolizer\n", - "\n", - "Term: Comparison Allele(s) or Genotype(s)\n", - "\n", - "- Content: Reference genotype used for comparison\n", - "- Manual Process: Find what the study variant was compared against\n", - "- Example: *1/*1, C (for wild-type comparisons)\n", - "\n", - "Term: Comparison Metabolizer types\n", - "\n", - "- Content: Reference metabolizer status for comparison\n", - "- Manual Process: Extract the comparison metabolizer phenotype\n", - "- Example: normal metabolizer\n", - "\n", - "Term: Specialty Population\n", - "\n", - "- Content: Age-specific populations\n", - "- Manual Process: Check if study specifically focused on:\n", - " - Pediatric: Children/adolescents\n", - " - Geriatric: Elderly patients\n", - " - Leave empty for general adult populations\n", - "\n", - "Term: Population types\n", - "- Content: Descriptor of study population\n", - "- Manual Process: Look for population descriptors, usually \"in people with\" or ethnicity information\n", - "- Example: in people with\n", - "\n", - "Term: Population Phenotypes or diseases\n", - "- Content: Disease/condition context with standardized prefix\n", - "- Manual Process: Find the medical condition studied, add appropriate prefix:\n", - " - Disease: for established diseases\n", - " - Other: for conditions/traits\n", - " - Side Effect: for adverse events\n", - "- Example: Other:Diabetes Mellitus, Type 2\n", - "\n", - "Term: isPlural\n", - "- Content: Grammar helper for sentence construction\n", - "- Manual Process: Use Is for singular subjects, Are for plural\n", - "- Example: Is\n", - "\n", - "Term: Is/Is Not associated\n", - "- Content: Direction of association\n", - "- Manual Process: Determine if association was:\n", - " - Associated with: Positive association found\n", - " - Not associated with: No association found\n", - "- Example: Associated with\n", - "\n", - "Term: Direction of effect\n", - "\n", - "- Content: Whether the effect increases or decreases the outcome\n", - "- Manual Process: Look for directional language:\n", - " - increased: Higher levels, better response, more effect\n", - " - decreased: Lower levels, worse response, less effect\n", - " - Leave empty if no clear direction\n", - "- Example: decreased\n", - "\n", - "Term: PD/PK terms\n", - "\n", - "- Content: Pharmacological outcome descriptor\n", - "- Manual Process: Extract the specific outcome measured:\n", - " - response to, concentrations of, metabolism of, clearance of, dose of\n", - "- Example: response to\n", - "\n", - "Term: Multiple drugs And/or\n", - "\n", - "- Content: Logical connector for multiple drugs\n", - "- Manual Process: If multiple drugs mentioned:\n", - " - and: All drugs together\n", - " - or: Any of the drugs\n", - " - Leave empty for single drug\n", - "\n", - "Term: Multiple phenotypes or diseases And/or\n", - "\n", - "- Content: Logical connector for multiple conditions\n", - "- Manual Process: Similar to drugs, use and/or for multiple conditions\n", - "- Leave empty for single condition\n", - "\n", - "General recommended strategies\n", - "\n", - "1. Scan for genetic variants: Look for \"rs\" numbers, gene names with asterisks, or phrases like \"genotype,\" \"allele,\"\n", - "\"polymorphism\"\n", - "2. Identify drug context: Find drug names in methods, results, or discussion sections\n", - "3. Locate outcome measures: Look for clinical endpoints, lab values, response rates, adverse events\n", - "4. Find statistical associations: Search for p-values, odds ratios, significant differences between genotype groups\n", - "5. Extract population details: Note the study population, disease context, and inclusion criteria\n", - "6. Standardize the relationship: Convert the finding into the standardized sentence format following the association pattern\n", - "\n", - "For each term, the output should be of the format:\n", - "\n", - "Extracted Output: (output)\n", - "Reason: (one sentence justification)\n", - "Quote: (quote from the article that demonstrates why)\n", + "From this article:\n", + "First, briefly summarize the methods and results of the study\n", + "Next, create a list of extract all variants that have a studied described effect on drug response.\n", + "For each variant <-> drug relationship in the list output the following:\n", + "Pair: The (variant, drug(s)) pair\n", + "Variant: The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18, etc.)\n", + "Gene: The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11, etc.)\n", + "Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.)\n", + "Relationship Description: Describe the drug(s) in this relationship\n", + "Variant Effect: Describe the outcome/effect found from the variant (drug efficacy, metabolism, toxicity, dosage, etc.)\n", + "Statistical Analysis: Describe the statistical analysis used and the reported p-values\n", + "Population Info: Describe the population of the study participants for this variant\n", + "Notes: Describe any other useful information included on this variant to understanding the study results.\n", + "\n", + "These 9 attributes should be separately repeated for every distinct (variant, drug(s)) relationship. For every attribute,\n", + "include the information as well as a quote from the article the information was concluded from.\n", "\"\"\"" ] }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 158, "metadata": {}, "outputs": [], "source": [ - "PHENO_EXTRACTION_PROMPT = \"\"\"\n", - "You are an expert pharmacogenomics researcher reading and extracting annotations related to how variants affect phenotypes from the following article\n", - "\n", - "\\n\\n{article_text}\\n\\n\n", - "\n", - "These are the following terms for which we need to extract values:\n", - "\n", - "## Terms for Extraction\n", - "\n", - "### Variant/Haplotypes\n", - "- **Content**: The specific genetic variant studied\n", - "- **Manual Process**: Extract SNP IDs (rs numbers), HLA alleles, star alleles, or genotype combinations\n", - "- **Example**: HLA-B*35:08, rs1801272, UGT1A1*1, UGT1A1*28\n", - "\n", - "### Gene\n", - "- **Content**: Gene symbol associated with the variant\n", - "- **Manual Process**: Find the gene name near the variant mention\n", - "- **Example**: HLA-B, CYP2A6, UGT1A1\n", - "\n", - "### Drug(s)\n", - "- **Content**: Drug(s) that caused or were involved in the phenotype\n", - "- **Manual Process**: \n", - " - Extract drug names that triggered the adverse event or phenotype\n", - " - Leave empty for disease susceptibility studies without drug involvement\n", - "- **Example**: lamotrigine, sacituzumab govitecan, empty for disease predisposition\n", - "\n", - "### Phenotype Category\n", - "- **Content**: Type of phenotype or outcome studied\n", - "- **Manual Process**: Categorize based on primary outcome:\n", - " - Toxicity: Adverse drug reactions, side effects, drug-induced toxicity\n", - " - Efficacy: Treatment response, therapeutic outcomes\n", - " - Metabolism/PK: Pharmacokinetic parameters, drug levels\n", - " - Dosage: Dose requirements, dose-response relationships\n", - " - Other: Disease susceptibility, traits not directly drug-related\n", - "- **Example**: \n", - " - Toxicity (for Stevens-Johnson Syndrome)\n", - " - Other (for alcoholism risk)\n", + "VARIANT_LIST_PROMPT = \"\"\"\n", + "You are an expert pharmacogenomics researcher reading and extracting annotations from the following article:\n", "\n", - "### Significance\n", - "- **Content**: Statistical significance of the association\n", - "- **Manual Process**: Look for p-values and statistical tests:\n", - " - yes: p < 0.05 or stated as significant\n", - " - no: p ≥ 0.05 or explicitly non-significant\n", - " - not stated: No statistical testing reported\n", - "- **Example**: no (for non-significant HLA associations)\n", + "{article_text}\n", "\n", - "### Notes\n", - "- **Content**: Key study details, statistics, methodology\n", - "- **Manual Process**: Extract relevant quotes showing statistical results, case descriptions, or important context\n", - "- **Example**: \"The allele was not significant when comparing allele frequency in cases...\"\n", - "\n", - "### Standardized Sentence\n", - "- **Content**: Standardized description of the genetic-phenotype association\n", - "- **Manual Process**: Write in format: \"[Variant] is [associated with/not associated with] [increased/decreased] [phenotype outcome] [drug context] [population context]\"\n", - "- **Example**: \"HLA-B *35:08 is not associated with likelihood of Maculopapular Exanthema, severe cutaneous adverse reactions or Stevens-Johnson Syndrome when treated with lamotrigine in people with Epilepsy.\"\n", - "\n", - "### Alleles\n", - "- **Content**: Specific allele or genotype if different from main variant field\n", - "- **Manual Process**: Extract the exact genotype mentioned\n", - "- **Example**: *35:08, AA + AT, *1/*28 + *28/*28\n", - "\n", - "### Specialty Population\n", - "- **Content**: Age-specific populations\n", - "- **Manual Process**: Identify if study focused on specific age groups:\n", - " - Pediatric: Children/adolescents\n", - " - Geriatric: Elderly patients\n", - " - Leave empty for general adult populations\n", - "- **Example**: Pediatric (for children with Fanconi Anemia)\n", - "\n", - "### Metabolizer Types\n", - "- **Content**: CYP enzyme phenotype when applicable\n", - "- **Manual Process**: Look for metabolizer classifications in CYP studies:\n", - " - poor metabolizer\n", - " - intermediate metabolizer\n", - " - extensive metabolizer\n", - " - ultrarapid metabolizer\n", - " - deficiency\n", - "- **Example**: ultrarapid metabolizer, intermediate activity\n", - "\n", - "### isPlural\n", - "- **Content**: Grammar helper for sentence construction\n", - "- **Manual Process**: Use Is for singular subjects, Are for plural\n", - "- **Example**: Is (for single allele), Are (for combined genotypes)\n", - "\n", - "### Is/Is Not Associated\n", - "- **Content**: Direction of statistical association\n", - "- **Manual Process**: Determine association type:\n", - " - Associated with: Positive association found\n", - " - Not associated with: No association found\n", - "- **Example**: Not associated with, Associated with\n", - "\n", - "### Direction of Effect\n", - "- **Content**: Whether the variant increases or decreases the phenotype\n", - "- **Manual Process**: Look for directional language:\n", - " - increased: Higher risk, more severe, greater likelihood\n", - " - decreased: Lower risk, less severe, reduced likelihood\n", - " - Leave empty if no clear direction\n", - "- **Example**: \n", - " - increased (for higher toxicity risk)\n", - " - decreased (for lower disease risk)\n", - "\n", - "### Side Effect/Efficacy/Other\n", - "- **Content**: Specific phenotype outcome with standardized prefix\n", - "- **Manual Process**: Categorize the phenotype and add appropriate prefix:\n", - " - Side Effect: for adverse drug reactions\n", - " - Efficacy: for therapeutic outcomes\n", - " - Disease: for disease conditions\n", - " - Other: for other traits/conditions\n", - " - PK: for pharmacokinetic measures\n", - "- **Example**: \n", - " - Side Effect:Stevens-Johnson Syndrome\n", - " - Disease:Alcohol abuse\n", - " - Other:Medication adherence\n", - "\n", - "### When Treated With/Exposed To/When Assayed With\n", - "- **Content**: Drug administration context\n", - "- **Manual Process**: Use standard phrases:\n", - " - when treated with: For therapeutic drug administration\n", - " - when exposed to: For environmental or non-therapeutic exposure\n", - " - due to: For substance-related disorders\n", - " - Leave empty for non-drug phenotypes\n", - "- **Example**: when treated with, due to (for substance abuse)\n", - "\n", - "### Multiple Drugs And/Or\n", - "- **Content**: Logical connector for multiple drugs\n", - "- **Manual Process**: If multiple drugs involved:\n", - " - and: Combination therapy\n", - " - or: Any of the drugs\n", - " - Leave empty for single drug\n", - "- **Example**: or (for any of several drugs)\n", - "\n", - "### Population Types\n", - "- **Content**: Description of study population\n", - "- **Manual Process**: Look for population descriptors:\n", - " - in people with: General population with condition\n", - " - in children with: Pediatric population\n", - " - in women with: Gender-specific population\n", - "- **Example**: in people with, in children with\n", - "\n", - "### Population Phenotypes or Diseases\n", - "- **Content**: Disease/condition context with prefix\n", - "- **Manual Process**: Find the medical condition and add prefix:\n", - " - Disease: for established diseases\n", - " - Other: for conditions/traits\n", - "- **Example**: \n", - " - Disease:Epilepsy\n", - " - Other:Diabetes Mellitus, Type 2\n", - "\n", - "### Multiple Phenotypes or Diseases And/Or\n", - "- **Content**: Logical connector for multiple conditions\n", - "- **Manual Process**: Use and/or for multiple disease contexts\n", - "- **Example**: and (for multiple comorbidities)\n", - "\n", - "### Comparison Allele(s) or Genotype(s)\n", - "- **Content**: Reference genotype for comparison\n", - "- **Manual Process**: Find what the variant was compared against\n", - "- **Example**: TT (wild-type), *1/*1 (normal function allele)\n", - "\n", - "### Comparison Metabolizer Types\n", - "- **Content**: Reference metabolizer phenotype\n", - "- **Manual Process**: Extract comparison metabolizer status\n", - "- **Example**: normal metabolizer\n", - "\n", - "## General Strategy Recommendations\n", - "\n", - "1. **Identify Phenotype Outcomes**: Look for adverse events, toxicities, disease conditions, clinical traits\n", - "2. **Find Genetic Associations**: Search for variants linked to the phenotype (may or may not involve drugs)\n", - "3. **Determine Drug Involvement**: Check if phenotype is drug-induced or related to disease susceptibility\n", - "4. **Extract Statistical Evidence**: Look for odds ratios, p-values, case reports, frequency differences\n", - "5. **Categorize Phenotype Type**: Classify as toxicity, efficacy, disease susceptibility, or other trait\n", - "6. **Note Population Context**: Identify specific patient populations, age groups, disease conditions\n", - "7. **Standardize the Relationship**: Convert findings into standardized sentence format describing the genetic-phenotype association\n", - "\n", - "For each term, the output should be of the format:\n", - "\n", - "Extracted Output: (output)\n", - "Reason: (one sentence justification)\n", - "Quote: (quote from the article that demonstrates why)\n", + "From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.) that are part of the article's\n", + "study/analysis. Include information on the gene group and allele (if present). Your output format should be a list of the variants with the following attributes:\n", + "Variant: The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18, etc.)\n", + "Gene: The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11, etc.)\n", + "Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.)\n", "\"\"\"" ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 159, "metadata": {}, "outputs": [], "source": [ - "FA_EXTRACTION_PROMPT = \"\"\"\n", - "You are an expert pharmacogenomics researcher reading and extracting annotations related to how variants affect in-vitro or lab measured mechanisms from the following article\n", - "\n", - "\\n\\n{article_text}\\n\\n\n", - "\n", - "These are the following terms for which we need to extract values:\n", - "\n", - "## Terms for Extraction\n", - "\n", - "### Variant/Haplotypes\n", - "- **Content**: The specific genetic variant studied\n", - "- **Manual Process**: Extract variant names, star alleles, SNP IDs, or protein constructs tested\n", - "- **Example**: CYP2C19*1, CYP2C19*17, rs72552763, CYP2B6*1, CYP2B6*6\n", - "\n", - "### Gene\n", - "- **Content**: Gene symbol associated with the variant\n", - "- **Manual Process**: Identify the gene being studied functionally\n", - "- **Example**: CYP2C19, CYP2B6, SLC22A1\n", - "\n", - "### Drug(s)\n", - "- **Content**: Substrate or compound used in the functional assay\n", - "- **Manual Process**: Extract the drug/substrate used to test enzyme activity or transport\n", - "- **Example**: normeperidine, bupropion, warfarin, voriconazole\n", - "\n", - "### Phenotype Category\n", - "- **Content**: Type of functional outcome measured\n", - "- **Manual Process**: Categorize based on what was measured:\n", - " - Metabolism/PK: Enzyme activity, clearance, transport, binding affinity\n", - " - Efficacy: Functional response in cellular systems\n", - " - Leave empty for basic biochemical studies\n", - "- **Example**: \n", - " - Metabolism/PK (for enzyme kinetics)\n", - " - Efficacy (for cellular response)\n", - "\n", - "### Significance\n", - "- **Content**: Statistical significance of functional differences\n", - "- **Manual Process**: Look for statistical comparisons:\n", - " - yes: Significant differences in activity/function\n", - " - no: No significant differences\n", - " - not stated: No statistical testing reported\n", - "- **Example**: \n", - " - yes (for significant activity differences)\n", - " - not stated (for descriptive studies)\n", - "\n", - "### Notes\n", - "- **Content**: Key experimental details, methodology, quantitative results\n", - "- **Manual Process**: Extract relevant quotes showing experimental conditions, numerical results, or important technical details\n", - "- **Example**: \"Clearance was 26.57% of wild-type. CYP2C19 variants expressed in Sf21 insect cells...\"\n", - "\n", - "### Standardized Sentence\n", - "- **Content**: Standardized description of the functional relationship\n", - "- **Manual Process**: Write in format: \"[Variant] is associated with [increased/decreased] [functional outcome] [experimental context] as compared to [reference variant]\"\n", - "- **Example**: \"CYP2C19 *17/*17 is associated with increased formation of normeperidine as compared to CYP2C19 *1/*1 + *1/*17.\"\n", - "\n", - "### Alleles\n", - "- **Content**: Specific allele or genotype tested\n", - "- **Manual Process**: Extract the exact variant designation\n", - "- **Example**: *17/*17, *1/*1, del, A\n", - "\n", - "### Metabolizer Types\n", - "- **Content**: Phenotype classification if applicable\n", - "- **Manual Process**: Rarely used in functional studies; mainly for CYP phenotyping\n", - "- **Example**: Usually empty\n", - "\n", - "### Comparison Allele(s) or Genotype(s)\n", - "- **Content**: Reference variant for comparison\n", - "- **Manual Process**: Find the control/wild-type variant used for comparison\n", - "- **Example**: *1/*1 + *1/*17, *1, GAT\n", - "\n", - "### Comparison Metabolizer Types\n", - "- **Content**: Reference metabolizer status\n", - "- **Manual Process**: Usually empty for functional studies\n", - "- **Example**: Usually empty\n", - "\n", - "### Assay Type\n", - "- **Content**: Laboratory method or experimental system used\n", - "- **Manual Process**: Extract the specific assay methodology:\n", - " - in human liver microsomes: Microsomal enzyme assays\n", - " - hydroxylation assay: Specific metabolic pathway assays\n", - " - crystal structure prediction: Computational modeling\n", - " - Leave empty if not specified\n", - "- **Example**: \n", - " - in human liver microsomes\n", - " - hydroxylation assay\n", - " - crystal structure prediction\n", - "\n", - "### Cell Type\n", - "- **Content**: Cell line or tissue system used for the assay\n", - "- **Manual Process**: Extract the specific cellular context:\n", - " - 293FT cells: Human embryonic kidney cells\n", - " - COS-7 cells: Monkey kidney cells\n", - " - Sf21 insect cells: Insect cells for baculovirus expression\n", - " - in insect microsomes: Microsomal preparations\n", - " - expressed in [cell type]: Heterologous expression systems\n", - "- **Example**: \n", - " - in 293FT cells\n", - " - expressed in COS-7 cells\n", - "\n", - "### Specialty Population\n", - "- **Content**: Age-specific populations (rarely applicable to functional studies)\n", - "- **Manual Process**: Usually leave empty for in vitro studies\n", - "- **Example**: Usually empty\n", - "\n", - "### isPlural\n", - "- **Content**: Grammar helper for sentence construction\n", - "- **Manual Process**: Use Is for singular subjects, Are for plural\n", - "- **Example**: Is\n", - "\n", - "### Is/Is Not Associated\n", - "- **Content**: Direction of functional association\n", - "- **Manual Process**: Determine association type:\n", - " - Associated with: Functional difference observed\n", - " - Not associated with: No functional difference\n", - "- **Example**: Associated with\n", - "\n", - "### Direction of Effect\n", - "- **Content**: Whether the variant increases or decreases function\n", - "- **Manual Process**: Look for directional language:\n", - " - increased: Higher activity, better function, enhanced capability\n", - " - decreased: Lower activity, reduced function, impaired capability\n", - "- **Example**: \n", - " - increased (for enhanced activity)\n", - " - decreased (for reduced activity)\n", - "\n", - "### Functional Terms\n", - "- **Content**: Specific functional outcome measured\n", - "- **Manual Process**: Extract the precise functional parameter:\n", - " - activity of: Enzyme activity measurements\n", - " - clearance of: Drug clearance kinetics\n", - " - formation of: Metabolite formation\n", - " - transport of: Transporter function\n", - " - affinity to: Binding affinity\n", - " - catalytic activity of: Catalytic efficiency\n", - "- **Example**: \n", - " - formation of\n", - " - activity of\n", - " - clearance of\n", - "\n", - "### Gene/Gene Product\n", - "- **Content**: Specific gene or protein being functionally assessed\n", - "- **Manual Process**: Extract the gene symbol when the functional term relates to gene product activity\n", - "- **Example**: CYP2C19, CYP2B6, CYP2C9\n", - "\n", - "### When Treated With/Exposed To/When Assayed With\n", - "- **Content**: Experimental substrate context\n", - "- **Manual Process**: Use standard phrases for functional assays:\n", - " - when assayed with: For enzyme activity assays\n", - " - of: For direct metabolite measurements\n", - " - Leave empty for non-substrate specific functions\n", - "- **Example**: \n", - " - when assayed with\n", - " - of\n", - "\n", - "### Multiple Drugs And/Or\n", - "- **Content**: Logical connector for multiple substrates\n", - "- **Manual Process**: If multiple substrates tested:\n", - " - and: Combination substrate assays\n", - " - or: Alternative substrate assays\n", - " - Leave empty for single substrate\n", - "- **Example**: or (for alternative substrates)\n", - "\n", - "## Manual Reading Strategy for Functional Annotations\n", - "\n", - "1. **Identify Experimental System**: Look for cell lines, microsomes, expression systems, computational models\n", - "2. **Find Functional Readouts**: Search for enzyme activity, kinetic parameters, binding affinity, transport rates\n", - "3. **Extract Substrate Information**: Identify the drug/compound used to test function\n", - "4. **Locate Comparison Data**: Find reference variants (usually wild-type or *1 alleles) for comparison\n", - "5. **Quantify Functional Changes**: Look for fold-changes, percentages, kinetic parameters (Km, Vmax, clearance)\n", - "6. **Note Experimental Conditions**: Extract assay conditions, expression systems, substrate concentrations\n", - "7. **Standardize the Relationship**: Convert findings into standardized sentence format describing the functional difference\n", - "\n", - "## Key Differences from Clinical Annotations\n", - "\n", - "- **Laboratory-based**: In vitro studies rather than patient studies\n", - "- **Mechanistic Focus**: How variants affect protein function rather than clinical outcomes\n", - "- **Quantitative Measures**: Enzyme kinetics, binding constants, activity percentages\n", - "- **Controlled Conditions**: Defined experimental systems rather than clinical populations\n", - "- **Substrate-specific**: Effects measured with specific drugs/compounds as substrates\n", - "\n", - "**Purpose**: Functional annotations provide the mechanistic basis for understanding why certain variants affect drug response in patients - they show how genetic changes alter protein function at the molecular level.\n", - "\n", - "For each term, the output should be of the format:\n", - "\n", - "Extracted Output: (output)\n", - "Reason: (one sentence justification)\n", - "Quote: (sentence from the article that demonstrates why)\n", - "\"\"\"" + "from dotenv import load_dotenv\n", + "load_dotenv()\n", + "from src.annotation_extraction.simple_inference import VariantList, Variant\n", + "import json\n", + "from loguru import logger" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "def save_output(prompt, output, filename):\n", + " # save prompt and output to file\n", + " with open(f\"test_outputs/{filename}.txt\", \"w\") as f:\n", + " f.write(\"Prompt:\\n\")\n", + " f.write(prompt)\n", + " f.write(\"\\nOutput:\\n\")\n", + " f.write(output)\n", + " logger.info(f\"Saved output to {filename}.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [], + "source": [ + "from src.annotation_extraction.simple_inference import Variant, VariantList" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-13 14:04:22.642\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m13\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-13 14:04:22.645\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m121\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" + ] + } + ], + "source": [ + "model = SimpleLLM()\n", + "article_text = ArticleParser(pmcid=\"PMC11730665\").get_article_text()\n", + "prompt_generator = PromptGenerator(VARIANT_LIST_PROMPT, {\"article_text\": article_text})\n", + "prompt = prompt_generator.get_prompt()\n", + "output = model.generate(prompt, response_format=VariantList)" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'VARIANT_LIST_PROMPT' from 'src.annotation_extraction.prompts' (/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/prompts.py)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[173]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcomponents\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m extract_variants_list\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/components.py:3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msimple_inference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variant, VariantList, SimpleLLM, PromptGenerator\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01marticle_parser\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ArticleParser\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mprompts\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m VARIANT_LIST_PROMPT\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mloguru\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m logger\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjson\u001b[39;00m\n", + "\u001b[31mImportError\u001b[39m: cannot import name 'VARIANT_LIST_PROMPT' from 'src.annotation_extraction.prompts' (/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/prompts.py)" + ] + } + ], + "source": [ + "from src.annotation_extraction.components import extract_variants_list" + ] + }, + { + "cell_type": "code", + "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "True" + "['rs2909451',\n", + " 'rs4664443',\n", + " 'rs3765467',\n", + " 'rs2285676',\n", + " 'rs163184',\n", + " 'rs7754840',\n", + " 'rs756992',\n", + " 'rs1799853',\n", + " 'rs1057910']" ] }, - "execution_count": 104, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from dotenv import load_dotenv\n", - "load_dotenv()" + "x = json.loads(output)['variant_list']\n", + "[i['variant_id'] for i in x]" ] }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-11 21:58:11.800\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m73\u001b[0m - \u001b[1mGetting article text from PMCID: PMC5728534\u001b[0m\n" + "\u001b[32m2025-06-12 14:57:23.200\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36msave_output\u001b[0m:\u001b[36m8\u001b[0m - \u001b[1mSaved output to variant_list_2.txt\u001b[0m\n" ] } ], - "source": [ - "model = SimpleLLM()\n", - "article_text = ArticleParser(pmcid=\"PMC5728534\").get_article_text()\n", - "prompt = PromptGenerator(FA_EXTRACTION_PROMPT, {\"article_text\": article_text}).get_prompt()\n", - "summary = model.generate(prompt)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], "source": [ "# save summary to file\n", - "with open(\"output_testing/fa_2.txt\", \"w\") as f:\n", - " f.write(summary)" + "save_output(prompt_generator.get_prompt_template(), output, \"variant_list_2\")" ] }, { diff --git a/src/annotation_extraction/article_parser.py b/src/annotation_extraction/article_parser.py new file mode 100644 index 0000000..e6049f8 --- /dev/null +++ b/src/annotation_extraction/article_parser.py @@ -0,0 +1,114 @@ +from loguru import logger +from typing import Optional +from dataclasses import dataclass +from pathlib import Path +@dataclass +class ArticleInput: + """Input article data.""" + title: str + article_text: str + pmid: str + pmcid: Optional[str] = None + +class ArticleParser: + """Convert Markdown article text or PMCID to ArticleInput. + + Args: + text: Optional[str] = None + pmcid: Optional[str] = None + remove_references: bool = True + """ + def __init__(self, text: Optional[str] = None, pmcid: Optional[str] = None, remove_references: bool = True): + self.text = text + self.pmcid = pmcid + self.remove_references = remove_references + if not self.text and not self.pmcid: + logger.error("Either text or pmcid must be provided.") + raise ValueError("Either text or pmcid must be provided.") + if self.text and self.pmcid: + logger.error("Only one of text or pmcid can be provided.") + raise ValueError("Only one of text or pmcid can be provided.") + if self.pmcid: + logger.info(f"Getting article text from PMCID: {self.pmcid}") + self.text = self.get_article_text() + if self.remove_references: + self.remove_references_section() + + def get_article_text(self) -> str: + """Get article text from PMCID.""" + article_path = Path("data") / "articles" / f"{self.pmcid}.md" + if not article_path.exists(): + logger.error(f"Article not found: {article_path}") + raise FileNotFoundError(f"Article not found: {article_path}") + with open(article_path, "r", encoding="utf-8") as f: + return f.read() + + def parse_title(self) -> str: + """Parse the title from the markdown text.""" + lines = self.text.split("\n") + if not lines: + return "" + title = lines[0].strip() + if title.startswith("# "): + title = title[2:].strip() + return title + + def remove_references_section(self): + """ + Removes the references section from article text. + + Returns: + str: Article text with references section removed + (Looks for ## References section and removes it and everything after) + """ + # Split the text into sections + sections = self.text.split("##") + + # Find the index of the References section + ref_index = -1 + for i, section in enumerate(sections): + if section.strip().startswith("References"): + ref_index = i + break + + # If references section found, remove it and everything after + if ref_index != -1: + sections = sections[:ref_index] + self.text = "##".join(sections) + + logger.info(f"Removed References section from article text") + + def parse_pmid(self) -> str: + """Parse the PMID from the markdown text.""" + lines = self.text.split("\n") + + # Look for PMID in metadata section + for line in lines: + if line.strip().startswith("**PMID:**"): + pmid = line.replace("**PMID:**", "").strip() + return pmid + + return "" + + def parse_pmcid(self) -> str: + """Parse the PMCID from the markdown text.""" + if self.pmcid: + return self.pmcid + lines = self.text.split("\n") + + # Look for PMCID in metadata section + for line in lines: + if line.strip().startswith("**PMCID:**"): + pmcid = line.replace("**PMCID:**", "").strip() + return pmcid + + return "" + + def parse(self) -> ArticleInput: + """Parse the article text into an ArticleInput.""" + return ArticleInput( + title=self.parse_title(), + article_text=self.text, + pmid=self.parse_pmid(), + pmcid=self.parse_pmcid(), + ) \ No newline at end of file diff --git a/src/annotation_extraction/components.py b/src/annotation_extraction/components.py new file mode 100644 index 0000000..9266075 --- /dev/null +++ b/src/annotation_extraction/components.py @@ -0,0 +1,43 @@ +from src.annotation_extraction.simple_inference import Variant, VariantList, SimpleLLM, PromptGenerator +from src.annotation_extraction.article_parser import ArticleParser +from loguru import logger +import json + +VARIANT_LIST_PROMPT = """ +You are an expert pharmacogenomics researcher reading and extracting annotations from the following article: + +{article_text} + +From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.) that are part of the article's +study/analysis. Include information on the gene group and allele (if present). Your output format should be a list of the variants with the following attributes: +Variant: The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18, etc.) +Gene: The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11, etc.) +Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.) +""" + +def extract_variants_list(article_text: str = None, pmcid: str = None) -> VariantList: + """Extract a list of variants from an article. + Args: + article_text: The text of the article. + PMCID: The PMCID of the article. + + Returns: + A list of variants. + """ + if article_text is None and pmcid is None: + logger.error("Either article_text or pmcid must be provided.") + raise ValueError("Either article_text or pmcid must be provided.") + + if article_text is not None and pmcid is not None: + logger.warning("Both article_text and PMCID are provided. Using article_text.") + + if article_text is None: + article_text = ArticleParser(pmcid=pmcid).get_article_text() + + model = SimpleLLM() + prompt_generator = PromptGenerator(VARIANT_LIST_PROMPT, {"article_text": article_text}) + prompt = prompt_generator.get_prompt() + output = model.generate(prompt, response_format=VariantList) + variant_list = json.loads(output)['variant_list'] + # variant_list_strs = [i['variant_id'] for i in variant_list] + return variant_list diff --git a/src/annotation_extraction/models.py b/src/annotation_extraction/models.py index e248e0b..73c2453 100644 --- a/src/annotation_extraction/models.py +++ b/src/annotation_extraction/models.py @@ -42,119 +42,6 @@ class Association(Enum): IS_NOT = "Is Not" ARE = "Are" ARE_NOT = "Are Not" - - -@dataclass -class ArticleInput: - """Input article data.""" - title: str - article_text: str - pmid: str - pmcid: Optional[str] = None - - -class ArticleParser: - """Convert Markdown article text or PMCID to ArticleInput. - - Args: - text: Optional[str] = None - pmcid: Optional[str] = None - remove_references: bool = True - """ - def __init__(self, text: Optional[str] = None, pmcid: Optional[str] = None, remove_references: bool = True): - self.text = text - self.pmcid = pmcid - if not self.text and not self.pmcid: - logger.error("Either text or pmcid must be provided.") - raise ValueError("Either text or pmcid must be provided.") - if self.text and self.pmcid: - logger.error("Only one of text or pmcid can be provided.") - raise ValueError("Only one of text or pmcid can be provided.") - if self.pmcid: - logger.info(f"Getting article text from PMCID: {self.pmcid}") - self.text = self.get_article_text() - if self.remove_references: - self.remove_references_section() - - def get_article_text(self) -> str: - """Get article text from PMCID.""" - article_path = Path("data") / "articles" / f"{self.pmcid}.md" - if not article_path.exists(): - logger.error(f"Article not found: {article_path}") - raise FileNotFoundError(f"Article not found: {article_path}") - with open(article_path, "r", encoding="utf-8") as f: - return f.read() - - def parse_title(self) -> str: - """Parse the title from the markdown text.""" - lines = self.text.split("\n") - if not lines: - return "" - title = lines[0].strip() - if title.startswith("# "): - title = title[2:].strip() - return title - - def remove_references_section(self): - """ - Removes the references section from article text. - - Returns: - str: Article text with references section removed - (Looks for ## References section and removes it and everything after) - """ - # Split the text into sections - sections = self.text.split("##") - - # Find the index of the References section - ref_index = -1 - for i, section in enumerate(sections): - if section.strip().startswith("References"): - ref_index = i - break - - # If references section found, remove it and everything after - if ref_index != -1: - sections = sections[:ref_index] - self.text = "##".join(sections) - - logger.info(f"Removed References section from article text") - - def parse_pmid(self) -> str: - """Parse the PMID from the markdown text.""" - lines = self.text.split("\n") - - # Look for PMID in metadata section - for line in lines: - if line.strip().startswith("**PMID:**"): - pmid = line.replace("**PMID:**", "").strip() - return pmid - - return "" - - def parse_pmcid(self) -> str: - """Parse the PMCID from the markdown text.""" - if self.pmcid: - return self.pmcid - lines = self.text.split("\n") - - # Look for PMCID in metadata section - for line in lines: - if line.strip().startswith("**PMCID:**"): - pmcid = line.replace("**PMCID:**", "").strip() - return pmcid - - return "" - - def parse(self) -> ArticleInput: - """Parse the article text into an ArticleInput.""" - return ArticleInput( - title=self.parse_title(), - article_text=self.text, - pmid=self.parse_pmid(), - pmcid=self.parse_pmcid(), - ) - @dataclass class RelevanceResult: """Result of relevance screening.""" diff --git a/src/annotation_extraction/simple_inference.py b/src/annotation_extraction/simple_inference.py index 8418de5..20d3860 100644 --- a/src/annotation_extraction/simple_inference.py +++ b/src/annotation_extraction/simple_inference.py @@ -5,6 +5,8 @@ import re import os from dotenv import load_dotenv +from pydantic import BaseModel, field_validator +from typing import List load_dotenv() class PromptGenerator: @@ -28,22 +30,86 @@ def generate_prompt(self) -> str: def get_prompt(self) -> str: return self.prompt - - - + + def get_prompt_template(self) -> str: + return self.prompt_template + class SimpleLLM(LLMInterface): - """Simple LLM interface that just returns the prompt.""" + """Simple LLM interface that just returns as response to the prompt.""" debug_mode = False def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): - self.model = model self.temperature = temperature if self.debug_mode: litellm.set_verbose = True + def generate(self, prompt: str, temperature: float = None, response_format: BaseModel = None) -> str: + temp = temperature if temperature is not None else self.temperature + response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], response_format=response_format, temperature=temp) + return response.choices[0].message.content + +class Variant(BaseModel): + """Variant.""" + variant_id: str + gene: str + allele: str + + # TODO: Add field validation for gene and allele + +class VariantList(BaseModel): + """List of variants.""" + variant_list: List[Variant] + +class StrEntry(BaseModel): + """String entry.""" + text: str + reason: str + quote: str + +class EnumEntry(BaseModel): + """Enum entry.""" + options: List[str] + text: str + reason: str + quote: str + + @field_validator('text') + @classmethod + def validate_text_in_options(cls, v: str, info) -> str: + if 'options' in info.data and v not in info.data['options']: + raise ValueError(f"Text {v} not in options {info.data['options']}") + return v + + +class VarDrugResponse(BaseModel): + """Response from VarDrug.""" + variant_id: StrEntry + drug_id: StrEntry + effect: StrEntry + evidence: StrEntry + +class VarPhenoResponse(BaseModel): + """Response from VarPheno.""" + variant_id: StrEntry + phenotype: StrEntry + evidence: StrEntry + +class VarFAResponse(BaseModel): + """Response from VarFA.""" + variant_id: StrEntry + phenotype: StrEntry + evidence: StrEntry + +class VarDrugParser(LLMInterface): + """LLM interface that parses a VarDrug response into a dictionary.""" + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): + super().__init__(model, temperature) + if self.debug_mode: + litellm.set_verbose = True + def generate(self, prompt: str, temperature: float = None) -> str: temp = temperature if temperature is not None else self.temperature - response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], temperature=temp) - return response.choices[0].message.content \ No newline at end of file + response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], structured_output=True, temperature=temp) + return response.choices[0].message.content diff --git a/src/annotation_extraction/utils.py b/src/annotation_extraction/utils.py new file mode 100644 index 0000000..87f4002 --- /dev/null +++ b/src/annotation_extraction/utils.py @@ -0,0 +1,7 @@ +import re + +# Example usage +def extractVariants(text): + # Note, seems to extract a ton of variants, not just the ones that are being studied + variantRegex = r'\b([A-Z]+\d+[A-Z]*\*\d+|\brs\d+)\b' + return re.findall(variantRegex, text) or [] \ No newline at end of file From 449ceaf54d5201caeef436fa7a93130eaa342da8 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Fri, 13 Jun 2025 20:09:28 -0700 Subject: [PATCH 08/28] feat: better list comparing --- data/benchmark/true_variant_list.json | 57184 ++++++++++++++++ docs/prompts/list_all_variants.txt | 5 +- notebooks/test.ipynb | 5969 +- pixi.lock | 20 +- pixi.toml | 1 + src/annotation_extraction/components.py | 8 +- src/annotation_extraction/simple_inference.py | 4 +- src/annotation_extraction/utils.py | 55 +- 8 files changed, 63203 insertions(+), 43 deletions(-) create mode 100644 data/benchmark/true_variant_list.json diff --git a/data/benchmark/true_variant_list.json b/data/benchmark/true_variant_list.json new file mode 100644 index 0000000..8a750b0 --- /dev/null +++ b/data/benchmark/true_variant_list.json @@ -0,0 +1,57184 @@ +{"PMC5712579": [ + { + "variant_id": "HLA-B*35:08", + "gene": "HLA-B", + "allele": "*35:08" + }, + { + "variant_id": "HLA-B*39:01", + "gene": "HLA-B", + "allele": "*39:01" + }, + { + "variant_id": "HLA-B*15:02", + "gene": "HLA-B", + "allele": "*15:02" + }, + { + "variant_id": "HLA-B*44:03", + "gene": "HLA-B", + "allele": "*44:03" + }, + { + "variant_id": "HLA-A*02:07", + "gene": "HLA-A", + "allele": "*02:07" + }, + { + "variant_id": "HLA-A*33:03", + "gene": "HLA-A", + "allele": "*33:03" + } + ], + "PMC3202555": [ + { + "variant_id": "rs1801272", + "gene": "CYP2A6", + "allele": "AA + AT" + }, + { + "variant_id": "rs1801272", + "gene": "CYP2A6", + "allele": "AT + TT" + }, + { + "variant_id": "CYP2A6*1, CYP2A6*12", + "gene": "CYP2A6", + "allele": "*12" + }, + { + "variant_id": "CYP2A6*1, CYP2A6*1x2", + "gene": "CYP2A6", + "allele": "*1x2" + }, + { + "variant_id": "CYP2B6*1, CYP2B6*9", + "gene": "CYP2B6", + "allele": "*9" + }, + { + "variant_id": "rs28399433", + "gene": "CYP2A6", + "allele": "A" + }, + { + "variant_id": "CYP2B6*1, CYP2B6*4", + "gene": "CYP2B6", + "allele": "*4" + }, + { + "variant_id": "rs8192789", + "gene": "CYP2A13", + "allele": "T" + }, + { + "variant_id": "rs1709083", + "gene": "CYP2A13", + "allele": "G" + }, + { + "variant_id": "CYP2B6*1, CYP2B6*4", + "gene": "CYP2B6", + "allele": "*1/*4 + *4/*4" + }, + { + "variant_id": "CYP2A6*1, CYP2A6*12", + "gene": "CYP2A6", + "allele": "*1/*1" + }, + { + "variant_id": "CYP2B6*1, CYP2B6*9", + "gene": "CYP2B6", + "allele": "*9" + }, + { + "variant_id": "rs72552266", + "gene": "CYP2A13", + "allele": "T" + }, + { + "variant_id": "rs578776", + "gene": "CHRNA3", + "allele": "A" + }, + { + "variant_id": "CYP2A6*1, CYP2A6*1x2", + "gene": "CYP2A6", + "allele": "*1/*1x2" + }, + { + "variant_id": "rs6311", + "gene": "HTR2A", + "allele": "C" + }, + { + "variant_id": "rs6928499", + "gene": "CNR1", + "allele": "G" + }, + { + "variant_id": "rs72552266", + "gene": "CYP2A13", + "allele": "CT" + }, + { + "variant_id": "rs8192789", + "gene": "CYP2A13", + "allele": "T" + }, + { + "variant_id": "rs16969968", + "gene": "CHRNA5", + "allele": "A" + }, + { + "variant_id": "rs1709083", + "gene": "CYP2A13", + "allele": "CG" + }, + { + "variant_id": "rs1799971", + "gene": "OPRM1", + "allele": "G" + }, + { + "variant_id": "rs1800497", + "gene": "ANKK1, DRD2", + "allele": "AA + AG" + }, + { + "variant_id": "rs28399433", + "gene": "CYP2A6", + "allele": "CC" + }, + { + "variant_id": "rs6311", + "gene": "HTR2A", + "allele": "C" + }, + { + "variant_id": "rs148044792", + "gene": "CYP2A13", + "allele": "A" + }, + { + "variant_id": "rs1799971", + "gene": "OPRM1", + "allele": "G" + }, + { + "variant_id": "rs1800497", + "gene": "ANKK1, DRD2", + "allele": "A" + }, + { + "variant_id": "rs6928499", + "gene": "CNR1", + "allele": "G" + }, + { + "variant_id": "rs148044792", + "gene": "CYP2A13", + "allele": "A" + }, + { + "variant_id": "rs16969968", + "gene": "CHRNA5", + "allele": "A" + }, + { + "variant_id": "rs578776", + "gene": "CHRNA3", + "allele": "A" + } + ], + "PMC11554802": [ + { + "variant_id": "UGT1A1*1, UGT1A1*28", + "gene": "UGT1A1", + "allele": "*1/*28 + *28/*28" + } + ], + "PMC9261480": [ + { + "variant_id": "ABCB1 intermediate activity", + "gene": "ABCB1", + "allele": null + }, + { + "variant_id": "CYP3A4 poor metabolizer", + "gene": "CYP3A4", + "allele": null + } + ], + "PMC11954185": [ + { + "variant_id": "HLA-A*02:01", + "gene": "HLA-A", + "allele": "*02:01" + } + ], + "PMC11936550": [ + { + "variant_id": "rs2279343", + "gene": "CYP2B6", + "allele": "AG + GG" + }, + { + "variant_id": "rs2279344", + "gene": "CYP2B6", + "allele": "AA + AG" + } + ], + "PMC6016699": [ + { + "variant_id": "rs804290", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs4840579", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs17153694", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs867858", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs11250159", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs12550668", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs2898292", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs6990313", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs10105409", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs6601604", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs10112596", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs13275657", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs2029969", + "gene": "GATA4", + "allele": "G" + }, + { + "variant_id": "rs3779664", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs2173117", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs3735814", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs17153698", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs6983129", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs2898295", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs11250163", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs17153747", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs13264774", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs804282", + "gene": "GATA4", + "allele": "G" + }, + { + "variant_id": "rs13273672", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs804280", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs2740434", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs2645399", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs11784693", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs804283", + "gene": "GATA4", + "allele": "G" + }, + { + "variant_id": "rs11785481", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs12458", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs3203358", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs3729856", + "gene": "GATA4", + "allele": "G" + }, + { + "variant_id": "rs1062219", + "gene": "GATA4", + "allele": "T" + } + ], + "PMC12035587": [ + { + "variant_id": "NUDT15*3", + "gene": "NUDT15", + "allele": "*3/*3" + } + ], + "PMC11918719": [ + { + "variant_id": "HLA-B*15:02", + "gene": "HLA-B", + "allele": "*15:02" + } + ], + "PMC3055642": [ + { + "variant_id": "rs615470", + "gene": "CHRNA5", + "allele": "T" + }, + { + "variant_id": "rs16969968", + "gene": "CHRNA5", + "allele": "A" + }, + { + "variant_id": "rs578776", + "gene": "CHRNA3", + "allele": "G" + }, + { + "variant_id": "rs578776", + "gene": "CHRNA3", + "allele": "G" + }, + { + "variant_id": "rs684513", + "gene": "CHRNA5", + "allele": "G" + }, + { + "variant_id": "rs684513", + "gene": "CHRNA5", + "allele": "G" + }, + { + "variant_id": "rs615470", + "gene": "CHRNA5", + "allele": "T" + } + ], + "PMC6562837": [ + { + "variant_id": "CYP2C19*1, CYP2C19*2", + "gene": "CYP2C19", + "allele": "*1/*2" + } + ], + "PMC5491091": [ + { + "variant_id": "rs8099917", + "gene": "IFNL3, IFNL4", + "allele": "TT" + } + ], + "PMC442471": [ + { + "variant_id": "DPYD deficiency", + "gene": "DPYD", + "allele": null + }, + { + "variant_id": "DPYD deficiency", + "gene": "DPYD", + "allele": null + } + ], + "PMC11948353": [ + { + "variant_id": "rs1041983", + "gene": "NAT2", + "allele": "T" + }, + { + "variant_id": "rs1799931", + "gene": "NAT2", + "allele": "A" + } + ], + "PMC11120674": [ + { + "variant_id": "rs2231142", + "gene": "ABCG2", + "allele": "GG" + }, + { + "variant_id": "rs1128503", + "gene": "ABCB1", + "allele": "AA" + }, + { + "variant_id": "rs1045642", + "gene": "ABCB1", + "allele": "AA" + } + ], + "PMC8315301": [ + { + "variant_id": "UGT1A1*1, UGT1A1*28", + "gene": "UGT1A1", + "allele": "*28/*28" + }, + { + "variant_id": "UGT1A1*1, UGT1A1*28", + "gene": "UGT1A1", + "allele": "*28/*28" + }, + { + "variant_id": "UGT1A1*1, UGT1A1*28", + "gene": "UGT1A1", + "allele": "*28/*28" + } + ], + "PMC9424318": [ + { + "variant_id": "UGT1A1*1, UGT1A1*28", + "gene": "UGT1A1", + "allele": "*28/*28" + } + ], + "PMC5748122": [ + { + "variant_id": "HLA-B*58:01", + "gene": "HLA-B", + "allele": "*58:01" + }, + { + "variant_id": "rs3099844", + "gene": "CYCSP5", + "allele": "AA + AC" + }, + { + "variant_id": "rs2734583", + "gene": null, + "allele": "AG + GG" + }, + { + "variant_id": "rs9263726", + "gene": "PSORS1C1", + "allele": "AA + AG" + } + ], + "PMC3904554": [ + { + "variant_id": "CYP2D6*4", + "gene": "CYP2D6", + "allele": "*4/*4" + }, + { + "variant_id": "CYP2D6*6", + "gene": "CYP2D6", + "allele": "*6/*6" + }, + { + "variant_id": "CYP2D6*5", + "gene": "CYP2D6", + "allele": "*5/*5" + }, + { + "variant_id": "CYP2D6*4, CYP2D6*5", + "gene": "CYP2D6", + "allele": "*4/*5" + }, + { + "variant_id": "CYP2D6*4, CYP2D6*7", + "gene": "CYP2D6", + "allele": "*4/*7" + }, + { + "variant_id": "CYP2D6*4, CYP2D6*6", + "gene": "CYP2D6", + "allele": "*4/*6" + }, + { + "variant_id": "CYP2D6*4, CYP2D6*11", + "gene": "CYP2D6", + "allele": "*4/*11" + }, + { + "variant_id": "CYP2D6*3, CYP2D6*4", + "gene": "CYP2D6", + "allele": "*3/*4" + }, + { + "variant_id": "CYP2D6*3, CYP2D6*5", + "gene": "CYP2D6", + "allele": "*3/*5" + }, + { + "variant_id": "CYP2D6*6, CYP2D6*7", + "gene": "CYP2D6", + "allele": "*6/*7" + }, + { + "variant_id": "CYP2D6*3, CYP2D6*6", + "gene": "CYP2D6", + "allele": "*3/*6" + }, + { + "variant_id": 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{ + "variant_id": "rs4646437", + "gene": "CYP3A4", + "allele": "G" + }, + { + "variant_id": "rs6956344", + "gene": "CYP3A4", + "allele": "C" + }, + { + "variant_id": "rs2242480", + "gene": "CYP3A4", + "allele": "C" + } + ], + "PMC2791978": [ + { + "variant_id": "rs1902023", + "gene": "UGT2B15", + "allele": "AA" + } + ], + "PMC3179823": [ + { + "variant_id": "rs9657182", + "gene": "IDO1", + "allele": "CC" + }, + { + "variant_id": "rs9657182", + "gene": "IDO1", + "allele": "CC" + } + ], + "PMC10612355": [ + { + "variant_id": "rs5751901", + "gene": "GGT1", + "allele": "CC" + }, + { + "variant_id": "rs2017869", + "gene": "GGT1", + "allele": "CC" + } + ], + "PMC6710518": [ + { + "variant_id": "HLA-B*27:05", + "gene": "HLA-B", + "allele": "*27:05" + }, + { + "variant_id": "HLA-B*38:02", + "gene": "HLA-B", + "allele": "*38:02" + }, + { + "variant_id": "HLA-DRB1*08:03", + "gene": "HLA-DRB1", + "allele": "*08:03" + } + ], + "PMC5749368": [ + { + "variant_id": "CYP2C9*1, CYP2C9*2, CYP2C9*3", + 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+ "gene": "CES1", + "allele": "C" + }, + { + "variant_id": "rs146456965", + "gene": "CES1", + "allele": "A" + } + ], + "PMC3227682": [ + { + "variant_id": "rs4148981", + "gene": "SLCO1A2", + "allele": "T" + } + ], + "PMC3876810": [ + { + "variant_id": "rs776746", + "gene": "CYP3A5", + "allele": "CC" + } + ], + "PMC10613299": [ + { + "variant_id": "rs762551", + "gene": "CYP1A2", + "allele": "AA + AC" + } + ], + "PMC6266131": [ + { + "variant_id": "rs9344", + "gene": "CCND1", + "allele": "AA + AG" + } + ], + "PMC549421": [ + { + "variant_id": "rs267606619", + "gene": "MT-RNR1", + "allele": "T" + } + ], + "PMC3564008": [ + { + "variant_id": "rs783145", + "gene": "PLG", + "allele": "G" + }, + { + "variant_id": "rs783145", + "gene": "PLG", + "allele": "G" + }, + { + "variant_id": "rs10871454", + "gene": null, + "allele": "C" + }, + { + "variant_id": "rs10277", + "gene": "SQSTM1", + "allele": "C" + }, + { + "variant_id": "rs884742", + "gene": "SLC22A3", + "allele": "C" + }, + { + "variant_id": "rs4751104", + "gene": "MGMT", + "allele": "G" + }, + { + "variant_id": "rs1007888", + "gene": "GSTT1", + "allele": "T" + }, + { + "variant_id": "rs10242455", + "gene": null, + "allele": "AG" + }, + { + "variant_id": "rs11861379", + "gene": "ABCC11", + "allele": "C" + } + ], + "PMC6906626": [ + { + "variant_id": "rs112563513", + "gene": "RYR1", + "allele": "A" + }, + { + "variant_id": "rs193922832", + "gene": "RYR1", + "allele": "A" + }, + { + "variant_id": "rs118192168", + "gene": "RYR1", + "allele": "A" + }, + { + "variant_id": "rs193922843", + "gene": "RYR1", + "allele": "T" + }, + { + "variant_id": "rs193922842", + "gene": "RYR1", + "allele": "G" + }, + { + "variant_id": "rs193922843", + "gene": "RYR1", + "allele": "T" + }, + { + "variant_id": "rs193922843", + "gene": "RYR1", + "allele": "T" + }, + { + "variant_id": "rs193922803", + "gene": "RYR1", + "allele": "T" + } + ], + "PMC10786722": [ + { + "variant_id": "rs56038477", + "gene": "DPYD", + "allele": "CT + TT" + }, + { + "variant_id": "rs1801160", + "gene": "DPYD", + "allele": "CT + TT" + }, + { + "variant_id": "rs2297595", + "gene": "DPYD", + "allele": "CC + CT" + } + ], + "PMC4168829": [ + { + "variant_id": "rs6817882", + "gene": "UGT2B17", + "allele": "C" + } + ], + "PMC4112558": [ + { + "variant_id": "rs115232898", + "gene": "DPYD", + "allele": "CT" + } + ], + "PMC3356662": [ + { + "variant_id": "rs772226819", + "gene": "CACNA1S", + "allele": "A" + } + ], + "PMC2955872": [ + { + "variant_id": "rs3093105", + "gene": "CYP4F2", + "allele": "C" + }, + { + "variant_id": "rs2108622", + "gene": "CYP4F2", + "allele": "T" + } + ], + "PMC4674354": [ + { + "variant_id": "rs35599367", + "gene": "CYP3A4", + "allele": "A" + } + ], + "PMC2680526": [ + { + "variant_id": "rs9024", + "gene": "CBR1", + "allele": "GG" + }, + { + "variant_id": "rs9024", + "gene": "CBR1", + "allele": "GG" + } + ], + "PMC4411934": [ + { + "variant_id": "rs4803217", + "gene": "IFNL3", + "allele": "C" + } + ], + "PMC6498441": [ + { + "variant_id": "rs1042008", + "gene": "SULT1A1", + "allele": "A" + }, + { + "variant_id": "rs767487725", + "gene": "SULT1A1", + "allele": "C" + }, + { + "variant_id": "rs544820732", + "gene": "SULT1A1", + "allele": "A" + }, + { + "variant_id": "rs72547527", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs767487725", + "gene": "SULT1A1", + "allele": "C" + }, + { + "variant_id": "rs758145522", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs72547527", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs1042008", + "gene": "SULT1A1", + "allele": "A" + }, + { + "variant_id": "rs765399160", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs28374453", + "gene": "SULT1A1", + "allele": "G" + }, + { + "variant_id": "rs28374453", + "gene": "SULT1A1", + "allele": "G" + }, + { + "variant_id": "rs765399160", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs552524124", + "gene": "SULT1A1", + "allele": "G" + }, + { + "variant_id": "rs758145522", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs1042028", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs765399160", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs758145522", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs544820732", + "gene": "SULT1A1", + "allele": "A" + }, + { + "variant_id": "rs767487725", + "gene": "SULT1A1", + "allele": "C" + }, + { + "variant_id": "rs1042008", + "gene": "SULT1A1", + "allele": "A" + }, + { + "variant_id": "rs72547527", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs552524124", + "gene": "SULT1A1", + "allele": "G" + }, + { + "variant_id": "rs1042028", + "gene": "SULT1A1", + "allele": "T" + }, + { + "variant_id": "rs544820732", + "gene": "SULT1A1", + "allele": "A" + }, + { + "variant_id": "rs552524124", + "gene": "SULT1A1", + "allele": "G" + }, + { + "variant_id": "rs28374453", + "gene": "SULT1A1", + "allele": "G" + }, + { + "variant_id": "rs1042028", + "gene": "SULT1A1", + "allele": "T" + } + ], + "PMC3602211": [ + { + "variant_id": "rs1801158", + "gene": "DPYD", + "allele": "TT" + }, + { + "variant_id": "rs1801159", + "gene": "DPYD", + "allele": "CC" + }, + { + "variant_id": "rs1801265", + "gene": "DPYD", + "allele": "GG" + }, + { + "variant_id": "rs55886062", + "gene": "DPYD", + "allele": "CC" + }, + { + "variant_id": "rs1801160", + "gene": "DPYD", + "allele": "TT" + }, + { + "variant_id": "rs3918290", + "gene": "DPYD", + "allele": "TT" + } + ] +} \ No newline at end of file diff --git a/docs/prompts/list_all_variants.txt b/docs/prompts/list_all_variants.txt index 4dd69a6..55543c6 100644 --- a/docs/prompts/list_all_variants.txt +++ b/docs/prompts/list_all_variants.txt @@ -3,4 +3,7 @@ You are an expert pharmacogenomics researcher reading and extracting annotations {article_text} From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.) that are part of the article's -study/analysis. Your output format should be a list of strings \ No newline at end of file +study/analysis. Include information on the gene group and allele (if present). Your output format should be a list of the variants with the following attributes: +Variant: The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18, etc.) +Gene: The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11, etc.) +Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.) \ No newline at end of file diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb index 5f4ed59..c3e0d85 100644 --- a/notebooks/test.ipynb +++ b/notebooks/test.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 283, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 286, "metadata": {}, "outputs": [], "source": [ @@ -189,53 +189,57 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 289, + "metadata": {}, + "outputs": [], + "source": [ + "from src.annotation_extraction.components import extract_variants_list" + ] + }, + { + "cell_type": "code", + "execution_count": 176, "metadata": {}, "outputs": [ { - "ename": "ImportError", - "evalue": "cannot import name 'VARIANT_LIST_PROMPT' from 'src.annotation_extraction.prompts' (/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/prompts.py)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[173]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcomponents\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m extract_variants_list\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/components.py:3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msimple_inference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variant, VariantList, SimpleLLM, PromptGenerator\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01marticle_parser\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ArticleParser\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mprompts\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m VARIANT_LIST_PROMPT\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mloguru\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m logger\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjson\u001b[39;00m\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'VARIANT_LIST_PROMPT' from 'src.annotation_extraction.prompts' (/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/prompts.py)" + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-13 14:11:26.359\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-13 14:11:26.361\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" ] } ], "source": [ - "from src.annotation_extraction.components import extract_variants_list" + "variant_list = extract_variants_list(pmcid=\"PMC11730665\")" ] }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 177, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['rs2909451',\n", - " 'rs4664443',\n", - " 'rs3765467',\n", - " 'rs2285676',\n", - " 'rs163184',\n", - " 'rs7754840',\n", - " 'rs756992',\n", - " 'rs1799853',\n", - " 'rs1057910']" + "[{'variant_id': 'rs2909451', 'gene': 'DPP4', 'allele': 'TT'},\n", + " {'variant_id': 'rs4664443', 'gene': 'DPP4', 'allele': 'GG'},\n", + " {'variant_id': 'rs3765467', 'gene': 'GLP1R', 'allele': 'AG'},\n", + " {'variant_id': 'rs2285676', 'gene': 'KCNJ11', 'allele': 'CC'},\n", + " {'variant_id': 'rs163184', 'gene': 'KCNQ1', 'allele': 'GG'},\n", + " {'variant_id': 'rs7754840', 'gene': 'CDKAL1', 'allele': 'CG'},\n", + " {'variant_id': 'rs756992', 'gene': 'CDKAL1', 'allele': 'AG'},\n", + " {'variant_id': 'rs1799853', 'gene': 'CYP2C9', 'allele': 'TT'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'GG'}]" ] }, - "execution_count": 171, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x = json.loads(output)['variant_list']\n", - "[i['variant_id'] for i in x]" + "variant_list" ] }, { @@ -256,6 +260,5919 @@ "save_output(prompt_generator.get_prompt_template(), output, \"variant_list_2\")" ] }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "pmcid_grouped = json.load(open(\"data/variantAnnotations/annotations_by_pmcid.json\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [], + "source": [ + "true_variant_list = {}\n", + "for paper in pmcid_grouped:\n", + " variants = []\n", + " variants.extend(paper['var_drug_ann'])\n", + " variants.extend(paper['var_pheno_ann'])\n", + " variants.extend(paper['var_fa_ann'])\n", + " pmcid = paper['pmcid']\n", + " variant_list = []\n", + " for variant in variants:\n", + " gene = variant['Gene']\n", + " variant_id = variant['Variant/Haplotypes']\n", + " allele = variant['Alleles']\n", + " try:\n", + " parsed_variant = Variant(variant_id=variant_id, gene=gene, allele=allele)\n", + " variant_list.append(parsed_variant)\n", + " except Exception as e:\n", + " logger.error(f\"Error parsing variant {variant_id} for PMCID {pmcid}: {e}\")\n", + " logger.error(f\"Variant ID: {variant_id}\")\n", + " logger.error(f\"Gene: {gene}\")\n", + " logger.error(f\"Allele: {allele}\")\n", + " true_variant_list[pmcid] = variant_list" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [], + "source": [ + "true_variant_list_json = {}\n", + "for key in true_variant_list:\n", + " true_variant_list_json[key] = []\n", + " for variant in true_variant_list[key]:\n", + " true_variant_list_json[key].append(variant.model_dump())" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'PMC5712579': [{'variant_id': 'HLA-B*35:08',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*35:08'},\n", + " {'variant_id': 'HLA-B*39:01', 'gene': 'HLA-B', 'allele': '*39:01'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*44:03', 'gene': 'HLA-B', 'allele': '*44:03'},\n", + " {'variant_id': 'HLA-A*02:07', 'gene': 'HLA-A', 'allele': '*02:07'},\n", + " {'variant_id': 'HLA-A*33:03', 'gene': 'HLA-A', 'allele': '*33:03'}],\n", + " 'PMC3202555': [{'variant_id': 'rs1801272',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': 'AA + AT'},\n", + " {'variant_id': 'rs1801272', 'gene': 'CYP2A6', 'allele': 'AT + TT'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*12', 'gene': 'CYP2A6', 'allele': '*12'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*1x2', 'gene': 'CYP2A6', 'allele': '*1x2'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*9', 'gene': 'CYP2B6', 'allele': '*9'},\n", + " {'variant_id': 'rs28399433', 'gene': 'CYP2A6', 'allele': 'A'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*4', 'gene': 'CYP2B6', 'allele': '*4'},\n", + " {'variant_id': 'rs8192789', 'gene': 'CYP2A13', 'allele': 'T'},\n", + " {'variant_id': 'rs1709083', 'gene': 'CYP2A13', 'allele': 'G'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*4',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*1/*4 + *4/*4'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*12', 'gene': 'CYP2A6', 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*9', 'gene': 'CYP2B6', 'allele': '*9'},\n", + " {'variant_id': 'rs72552266', 'gene': 'CYP2A13', 'allele': 'T'},\n", + " {'variant_id': 'rs578776', 'gene': 'CHRNA3', 'allele': 'A'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*1x2',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*1x2'},\n", + " {'variant_id': 'rs6311', 'gene': 'HTR2A', 'allele': 'C'},\n", + " {'variant_id': 'rs6928499', 'gene': 'CNR1', 'allele': 'G'},\n", + " {'variant_id': 'rs72552266', 'gene': 'CYP2A13', 'allele': 'CT'},\n", + " {'variant_id': 'rs8192789', 'gene': 'CYP2A13', 'allele': 'T'},\n", + " {'variant_id': 'rs16969968', 'gene': 'CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs1709083', 'gene': 'CYP2A13', 'allele': 'CG'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1800497', 'gene': 'ANKK1, DRD2', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs28399433', 'gene': 'CYP2A6', 'allele': 'CC'},\n", + " {'variant_id': 'rs6311', 'gene': 'HTR2A', 'allele': 'C'},\n", + " {'variant_id': 'rs148044792', 'gene': 'CYP2A13', 'allele': 'A'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1800497', 'gene': 'ANKK1, DRD2', 'allele': 'A'},\n", + " {'variant_id': 'rs6928499', 'gene': 'CNR1', 'allele': 'G'},\n", + " {'variant_id': 'rs148044792', 'gene': 'CYP2A13', 'allele': 'A'},\n", + " {'variant_id': 'rs16969968', 'gene': 'CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs578776', 'gene': 'CHRNA3', 'allele': 'A'}],\n", + " 'PMC11554802': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'}],\n", + " 'PMC9261480': [{'variant_id': 'ABCB1 intermediate activity',\n", + " 'gene': 'ABCB1',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP3A4 poor metabolizer', 'gene': 'CYP3A4', 'allele': None}],\n", + " 'PMC11954185': [{'variant_id': 'HLA-A*02:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*02:01'}],\n", + " 'PMC11936550': [{'variant_id': 'rs2279343',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs2279344', 'gene': 'CYP2B6', 'allele': 'AA + AG'}],\n", + " 'PMC6016699': [{'variant_id': 'rs804290', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs4840579', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs17153694', 'gene': 'GATA4', 'allele': 'T'},\n", + " {'variant_id': 'rs867858', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs11250159', 'gene': 'GATA4', 'allele': 'T'},\n", + " {'variant_id': 'rs12550668', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs2898292', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs6990313', 'gene': 'GATA4', 'allele': 'T'},\n", + " {'variant_id': 'rs10105409', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs6601604', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs10112596', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs13275657', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs2029969', 'gene': 'GATA4', 'allele': 'G'},\n", + " {'variant_id': 'rs3779664', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs2173117', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs3735814', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs17153698', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs6983129', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs2898295', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs11250163', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs17153747', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs13264774', 'gene': 'GATA4', 'allele': 'T'},\n", + " {'variant_id': 'rs804282', 'gene': 'GATA4', 'allele': 'G'},\n", + " {'variant_id': 'rs13273672', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs804280', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs2740434', 'gene': 'GATA4', 'allele': 'A'},\n", + " {'variant_id': 'rs2645399', 'gene': 'GATA4', 'allele': 'T'},\n", + " {'variant_id': 'rs11784693', 'gene': 'GATA4', 'allele': 'T'},\n", + " {'variant_id': 'rs804283', 'gene': 'GATA4', 'allele': 'G'},\n", + " {'variant_id': 'rs11785481', 'gene': 'GATA4', 'allele': 'T'},\n", + " {'variant_id': 'rs12458', 'gene': 'GATA4', 'allele': 'T'},\n", + " {'variant_id': 'rs3203358', 'gene': 'GATA4', 'allele': 'C'},\n", + " {'variant_id': 'rs3729856', 'gene': 'GATA4', 'allele': 'G'},\n", + " {'variant_id': 'rs1062219', 'gene': 'GATA4', 'allele': 'T'}],\n", + " 'PMC12035587': [{'variant_id': 'NUDT15*3',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*3/*3'}],\n", + " 'PMC11918719': [{'variant_id': 'HLA-B*15:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02'}],\n", + " 'PMC3055642': [{'variant_id': 'rs615470', 'gene': 'CHRNA5', 'allele': 'T'},\n", + " {'variant_id': 'rs16969968', 'gene': 'CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs578776', 'gene': 'CHRNA3', 'allele': 'G'},\n", + " {'variant_id': 'rs578776', 'gene': 'CHRNA3', 'allele': 'G'},\n", + " {'variant_id': 'rs684513', 'gene': 'CHRNA5', 'allele': 'G'},\n", + " {'variant_id': 'rs684513', 'gene': 'CHRNA5', 'allele': 'G'},\n", + " {'variant_id': 'rs615470', 'gene': 'CHRNA5', 'allele': 'T'}],\n", + " 'PMC6562837': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2'}],\n", + " 'PMC5491091': [{'variant_id': 'rs8099917',\n", + " 'gene': 'IFNL3, IFNL4',\n", + " 'allele': 'TT'}],\n", + " 'PMC442471': [{'variant_id': 'DPYD deficiency',\n", + " 'gene': 'DPYD',\n", + " 'allele': None},\n", + " {'variant_id': 'DPYD deficiency', 'gene': 'DPYD', 'allele': None}],\n", + " 'PMC11948353': [{'variant_id': 'rs1041983', 'gene': 'NAT2', 'allele': 'T'},\n", + " {'variant_id': 'rs1799931', 'gene': 'NAT2', 'allele': 'A'}],\n", + " 'PMC11120674': [{'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GG'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'}],\n", + " 'PMC8315301': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'}],\n", + " 'PMC9424318': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'}],\n", + " 'PMC5748122': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'},\n", + " {'variant_id': 'rs3099844', 'gene': 'CYCSP5', 'allele': 'AA + AC'},\n", + " {'variant_id': 'rs2734583', 'gene': None, 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs9263726', 'gene': 'PSORS1C1', 'allele': 'AA + AG'}],\n", + " 'PMC3904554': [{'variant_id': 'CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*6', 'gene': 'CYP2D6', 'allele': '*6/*6'},\n", + " {'variant_id': 'CYP2D6*5', 'gene': 'CYP2D6', 'allele': '*5/*5'},\n", + " {'variant_id': 'CYP2D6*4, CYP2D6*5', 'gene': 'CYP2D6', 'allele': '*4/*5'},\n", + " {'variant_id': 'CYP2D6*4, CYP2D6*7', 'gene': 'CYP2D6', 'allele': '*4/*7'},\n", + " {'variant_id': 'CYP2D6*4, CYP2D6*6', 'gene': 'CYP2D6', 'allele': '*4/*6'},\n", + " {'variant_id': 'CYP2D6*4, CYP2D6*11', 'gene': 'CYP2D6', 'allele': '*4/*11'},\n", + " {'variant_id': 'CYP2D6*3, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*3/*4'},\n", + " {'variant_id': 'CYP2D6*3, CYP2D6*5', 'gene': 'CYP2D6', 'allele': '*3/*5'},\n", + " {'variant_id': 'CYP2D6*6, CYP2D6*7', 'gene': 'CYP2D6', 'allele': '*6/*7'},\n", + " {'variant_id': 'CYP2D6*3, CYP2D6*6', 'gene': 'CYP2D6', 'allele': '*3/*6'},\n", + " {'variant_id': 'CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*7, CYP2D6*11',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*4 + *3/*5 + *3/*6 + *4/*11 + *4/*4 + *4/*5 + *4/*6 + *4/*7 + *5/*5 + *6/*6 + *6/*7'},\n", + " {'variant_id': 'CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*7, CYP2D6*11',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*4 + *3/*5 + *3/*6 + *4/*11 + *4/*4 + *4/*5 + *4/*6 + *4/*7 + *5/*5 + *6/*6 + *6/*7'},\n", + " {'variant_id': 'CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*7, CYP2D6*11',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*4 + *3/*5 + *3/*6 + *4/*11 + *4/*4 + *4/*5 + *4/*6 + *4/*7 + *5/*5 + *6/*6 + *6/*7'}],\n", + " 'PMC3237759': [{'variant_id': 'rs1851426', 'gene': 'CYP3A4', 'allele': 'G'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'T'},\n", + " {'variant_id': 'rs2687116', 'gene': 'CYP3A4', 'allele': 'A'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'C'},\n", + " {'variant_id': 'rs2257401', 'gene': 'CYP3A7', 'allele': 'C'},\n", + " {'variant_id': 'rs4646437', 'gene': 'CYP3A4', 'allele': 'G'},\n", + " {'variant_id': 'rs6956344', 'gene': 'CYP3A4', 'allele': 'C'},\n", + " {'variant_id': 'rs2242480', 'gene': 'CYP3A4', 'allele': 'C'}],\n", + " 'PMC2791978': [{'variant_id': 'rs1902023',\n", + " 'gene': 'UGT2B15',\n", + " 'allele': 'AA'}],\n", + " 'PMC3179823': [{'variant_id': 'rs9657182', 'gene': 'IDO1', 'allele': 'CC'},\n", + " {'variant_id': 'rs9657182', 'gene': 'IDO1', 'allele': 'CC'}],\n", + " 'PMC10612355': [{'variant_id': 'rs5751901', 'gene': 'GGT1', 'allele': 'CC'},\n", + " {'variant_id': 'rs2017869', 'gene': 'GGT1', 'allele': 'CC'}],\n", + " 'PMC6710518': [{'variant_id': 'HLA-B*27:05',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*27:05'},\n", + " {'variant_id': 'HLA-B*38:02', 'gene': 'HLA-B', 'allele': '*38:02'},\n", + " {'variant_id': 'HLA-DRB1*08:03', 'gene': 'HLA-DRB1', 'allele': '*08:03'}],\n", + " 'PMC5749368': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'TT'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'TT'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'TT'}],\n", + " 'PMC7586802': [{'variant_id': 'rs1799963', 'gene': 'F2', 'allele': 'A'},\n", + " {'variant_id': 'rs2066865', 'gene': 'FGG', 'allele': 'A'},\n", + " {'variant_id': 'rs6025', 'gene': 'F5', 'allele': 'T'}],\n", + " 'PMC2432489': [{'variant_id': 'rs11188072',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': 'TT'},\n", + " {'variant_id': 'rs12248560', 'gene': 'CYP2C19', 'allele': 'TT'}],\n", + " 'PMC9532634': [{'variant_id': 'rs35599367',\n", + " 'gene': 'CYP3A4',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs4823613', 'gene': 'PPARA', 'allele': 'GG'},\n", + " {'variant_id': 'rs4253728', 'gene': 'PPARA', 'allele': 'GG'},\n", + " {'variant_id': 'rs35599367', 'gene': 'CYP3A4', 'allele': 'AA + AG'}],\n", + " 'PMC7689744': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'}],\n", + " 'PMC3513236': [{'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*10', 'gene': 'CYP2D6', 'allele': '*10/*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'}],\n", + " 'PMC7274090': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*8, CYP2C19*9',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3 + *8 + *9'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*8, CYP2C19*9',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3 + *8 + *9'},\n", + " {'variant_id': 'CYP2C19*1', 'gene': 'CYP2C19', 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*8, CYP2C19*9',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3 + *8 + *9'}],\n", + " 'PMC6379053': [{'variant_id': 'HLA-DQA1*02:01',\n", + " 'gene': 'HLA-DQA1',\n", + " 'allele': '*02:01'},\n", + " {'variant_id': 'HLA-DRB1*07:01', 'gene': 'HLA-DRB1', 'allele': '*07:01'},\n", + " {'variant_id': 'rs2647087', 'gene': None, 'allele': 'C'}],\n", + " 'PMC4181635': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*6 + *6/*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'}],\n", + " 'PMC3376439': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*3'}],\n", + " 'PMC5591929': [{'variant_id': 'NAT2 intermediate acetylator',\n", + " 'gene': 'NAT2',\n", + " 'allele': None}],\n", + " 'PMC5753614': [{'variant_id': 'rs746071566',\n", + " 'gene': 'NUDT15',\n", + " 'allele': 'GGAGTC'},\n", + " {'variant_id': 'TPMT*1, TPMT*6', 'gene': 'TPMT', 'allele': '*6'},\n", + " {'variant_id': 'TPMT*1, TPMT*3C', 'gene': 'TPMT', 'allele': '*3C'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'T'}],\n", + " 'PMC6699898': [{'variant_id': 'rs1076560', 'gene': 'DRD2', 'allele': 'A'},\n", + " {'variant_id': 'rs12364283', 'gene': 'DRD2', 'allele': 'G'},\n", + " {'variant_id': 'rs2283265', 'gene': 'DRD2', 'allele': 'A'}],\n", + " 'PMC3105036': [{'variant_id': 'rs2125739', 'gene': 'ABCC10', 'allele': 'C'},\n", + " {'variant_id': 'rs9349256', 'gene': 'ABCC10', 'allele': 'G'}],\n", + " 'PMC3640375': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'}],\n", + " 'PMC3793390': [{'variant_id': 'rs11322783',\n", + " 'gene': 'IFNL4',\n", + " 'allele': 'TT/TT'},\n", + " {'variant_id': 'rs11322783', 'gene': 'IFNL4', 'allele': 'TT/TT'},\n", + " {'variant_id': 'rs12979860', 'gene': 'IFNL3, IFNL4', 'allele': 'CC'},\n", + " {'variant_id': 'rs12979860', 'gene': 'IFNL3, IFNL4', 'allele': 'CC'}],\n", + " 'PMC9026289': [{'variant_id': 'rs72552763',\n", + " 'gene': 'SLC22A1',\n", + " 'allele': 'del'}],\n", + " 'PMC1885048': [{'variant_id': 'CYP2D6*1, CYP2D6*4, CYP2D6*5',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*5'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2xN', 'gene': 'CYP2D6', 'allele': '*2xN'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*5', 'gene': 'CYP2D6', 'allele': '*5'}],\n", + " 'PMC3080646': [{'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*2 + *1/*3A + *1/*3C'},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'AC'}],\n", + " 'PMC2896757': [{'variant_id': 'TPMT*1, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*3A + *3A/*3A + *1/*3C + *3C/*3C'},\n", + " {'variant_id': 'TPMT*1, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*3A + *3A/*3A + *1/*3C + *3C/*3C'},\n", + " {'variant_id': 'TPMT*1, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*3A + *3A/*3A + *1/*3C + *3C/*3C'},\n", + " {'variant_id': 'TPMT*1, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*3A + *3A/*3A + *1/*3C + *3C/*3C'}],\n", + " 'PMC3791433': [{'variant_id': 'CYP2D6*1, CYP2D6*4, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4 + *10/*10'}],\n", + " 'PMC4104276': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*8', 'gene': 'CYP2C9', 'allele': '*8'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*9', 'gene': 'CYP2C19', 'allele': '*9'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*17/*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*6', 'gene': 'CYP2B6', 'allele': '*6'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*5', 'gene': 'CYP2B6', 'allele': '*5'},\n", + " {'variant_id': 'rs2069514', 'gene': 'CYP1A2', 'allele': 'A'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*13', 'gene': 'CYP2C19', 'allele': '*13'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'rs2069514', 'gene': 'CYP1A2', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'C'}],\n", + " 'PMC2879626': [{'variant_id': 'CYP2A6*1, CYP2A6*4, CYP2A6*7, CYP2A6*9',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*4 + *1/*7 + *1/*9 + *4/*4 + *7/*7 + *9/*9'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*4, CYP2A6*7, CYP2A6*9',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*4 + *1/*7 + *1/*9 + *4/*4 + *7/*7 + *9/*9'}],\n", + " 'PMC5301237': [{'variant_id': 'rs11322783',\n", + " 'gene': 'IFNL4',\n", + " 'allele': 'TT/TT'},\n", + " {'variant_id': 'rs12979860', 'gene': 'IFNL3, IFNL4', 'allele': 'CC'}],\n", + " 'PMC3698861': [{'variant_id': 'CYP2A6*1, CYP2A6*46',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*46 + *46/*46'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*2, CYP2A6*4, CYP2A6*9, CYP2A6*12, CYP2A6*17, CYP2A6*20, CYP2A6*23, CYP2A6*24, CYP2A6*25, CYP2A6*26, CYP2A6*27, CYP2A6*28, CYP2A6*35',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*2 + *1/*4 + *1/*17 + *1/*20 + *1/*23 + *1/*24 + *1/*25 + *1/*26 + *1/*27 + *1/*28 + *1/*35 + *9/*9 + *17/*17 + *20/*20 + *35/*35'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*2, CYP2A6*4, CYP2A6*9, CYP2A6*12, CYP2A6*17, CYP2A6*20, CYP2A6*23, CYP2A6*24, CYP2A6*25, CYP2A6*26, CYP2A6*27, CYP2A6*28, CYP2A6*35, CYP2A6*46',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*2 + *1/*4 + *1/*9 + *1/*12 + *1/*17 + *1/*20 + *1/*23 + *1/*24 + *1/*25 + *1/*26 + *1/*27 + *1/*28 + *1/*35 + *9/*9 + *17/*17 + *20/*20 + *35/*35'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*2, CYP2A6*4, CYP2A6*9, CYP2A6*12, CYP2A6*17, CYP2A6*20, CYP2A6*23, CYP2A6*24, CYP2A6*25, CYP2A6*26, CYP2A6*27, CYP2A6*28, CYP2A6*35, CYP2A6*46',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*2 + *1/*4 + *1/*17 + *1/*20 + *1/*23 + *1/*24 + *1/*25 + *1/*26 + *1/*27 + *1/*28 + *1/*35 + *9/*9 + *17/*17 + *20/*20 + *35/*35'}],\n", + " 'PMC11917620': [{'variant_id': 'HLA-B*15:18, HLA-B*40:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:18/*40:01'}],\n", + " 'PMC8678305': [{'variant_id': 'NUDT15*1, NUDT15*2, NUDT15*3',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*1/*2 + *1/*3'},\n", + " {'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C, TPMT*8, TPMT*9, TPMT*12, TPMT*24',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*2 + *1/*3A +*1/*3C +*1/*8 +*1/*9 +*1/*12 +*1/*24 +*2/*3A +*3A/*3A +*3C/*3C'}],\n", + " 'PMC5065345': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs1801265', 'gene': 'DPYD', 'allele': 'AG'}],\n", + " 'PMC9303592': [{'variant_id': 'SLCO1B1*1, SLCO1B1*5',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*1/*5 + *5/*5'}],\n", + " 'PMC5071174': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'}],\n", + " 'PMC3063997': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR long form (L allele)'},\n", + " {'variant_id': 'rs1042173', 'gene': 'SLC6A4', 'allele': 'AA'}],\n", + " 'PMC9810307': [{'variant_id': 'rs2612091', 'gene': 'ENOSF1', 'allele': 'CC'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'}],\n", + " 'PMC3872961': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR long form (L allele)'}],\n", + " 'PMC9322346': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*11',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*11 + *2/*11'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*11',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*11 + *2/*11'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*11', 'gene': 'CYP2C9', 'allele': '*1/*11'}],\n", + " 'PMC2696766': [{'variant_id': 'rs11280056',\n", + " 'gene': 'TYMS',\n", + " 'allele': 'TTAAAGTTA'},\n", + " {'variant_id': 'rs11280056', 'gene': 'TYMS', 'allele': 'TTAAAGTTA'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTCGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTCGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'A'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'GG'}],\n", + " 'PMC5980573': [{'variant_id': 'UGT1A1*1, UGT1A1*28, UGT1A1*37',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28 + *28/*37'}],\n", + " 'PMC3471396': [{'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'C'},\n", + " {'variant_id': 'rs2413739', 'gene': 'PACSIN2', 'allele': 'T'},\n", + " {'variant_id': 'rs11045879', 'gene': 'SLCO1B1', 'allele': 'T'},\n", + " {'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'CT'},\n", + " {'variant_id': 'rs2413739', 'gene': 'PACSIN2', 'allele': 'C'}],\n", + " 'PMC2793040': [{'variant_id': 'rs11045879', 'gene': 'SLCO1B1', 'allele': 'T'},\n", + " {'variant_id': 'rs4149081', 'gene': 'SLCO1B1', 'allele': 'G'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'C'},\n", + " {'variant_id': 'rs11045879', 'gene': 'SLCO1B1', 'allele': 'T'},\n", + " {'variant_id': 'rs4149081', 'gene': 'SLCO1B1', 'allele': 'G'}],\n", + " 'PMC5520099': [{'variant_id': 'rs11280056', 'gene': 'TYMS', 'allele': 'del'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'TT'},\n", + " {'variant_id': 'rs75017182', 'gene': 'DPYD', 'allele': 'GG'},\n", + " {'variant_id': 'rs1801158', 'gene': 'DPYD', 'allele': 'CC'},\n", + " {'variant_id': 'rs55886062', 'gene': 'DPYD', 'allele': 'AA'}],\n", + " 'PMC9537744': [{'variant_id': 'GSTT1 non-null, GSTT1 null',\n", + " 'gene': 'GSTT1',\n", + " 'allele': None},\n", + " {'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': None},\n", + " {'variant_id': 'rs143731390', 'gene': 'CYP2A6', 'allele': 'A'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs3957357', 'gene': 'GSTA1', 'allele': 'A'},\n", + " {'variant_id': 'rs1051775', 'gene': 'GSTA1', 'allele': 'C'},\n", + " {'variant_id': 'rs1051775', 'gene': 'GSTA1', 'allele': 'C'},\n", + " {'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'T'}],\n", + " 'PMC9576471': [{'variant_id': 'UGT1A7*1a, UGT1A7*2, UGT1A7*3',\n", + " 'gene': 'UGT1A7',\n", + " 'allele': '*1a/*3 + *2/*3 + *3/*3'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*6 + *6/*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*27, UGT1A1*28, UGT1A1*60',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*27 + *28 + *60'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*6 + *6/*6'},\n", + " {'variant_id': 'UGT1A7*1a, UGT1A7*2, UGT1A7*3',\n", + " 'gene': 'UGT1A7',\n", + " 'allele': '*1a/*3 + *2/*3 + *3/*3'},\n", + " {'variant_id': 'rs3832043', 'gene': 'UGT1A9', 'allele': 'T/del + del/del'},\n", + " {'variant_id': 'rs3832043', 'gene': 'UGT1A9', 'allele': 'T/del + del/del'}],\n", + " 'PMC6021964': [{'variant_id': 'GSTT1 non-null, GSTT1 null',\n", + " 'gene': 'GSTT1',\n", + " 'allele': None}],\n", + " 'PMC2675574': [{'variant_id': 'rs1801252',\n", + " 'gene': 'ADRB1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1801252', 'gene': 'ADRB1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1801253', 'gene': 'ADRB1', 'allele': 'CC + CG'},\n", + " {'variant_id': 'rs1801253', 'gene': 'ADRB1', 'allele': 'CC + CG'}],\n", + " 'PMC6989102': [{'variant_id': 'CYP2C9*1, CYP2C9*2',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *2/*2 + *2/*3 + *3/*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *2/*2 + *2/*3 + *3/*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*1/*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *2/*2 + *2/*3 + *3/*3'}],\n", + " 'PMC9534651': [{'variant_id': 'CYP2A6*4',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*4/*4'},\n", + " {'variant_id': 'rs3760657', 'gene': 'CYP2B6', 'allele': 'AA'},\n", + " {'variant_id': 'rs3100', 'gene': 'UGT2B15', 'allele': 'AA'},\n", + " {'variant_id': 'rs11726322', 'gene': 'UGT2B10', 'allele': 'CC'}],\n", + " 'PMC3992871': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*1/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'}],\n", + " 'PMC6421934': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28'},\n", + " {'variant_id': 'rs238406', 'gene': 'ERCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs238406', 'gene': 'ERCC2', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs1799793', 'gene': 'ERCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs1799793', 'gene': 'ERCC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs1050565', 'gene': 'BLMH', 'allele': 'CC'},\n", + " {'variant_id': 'rs1050565', 'gene': 'BLMH', 'allele': 'CC'},\n", + " {'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'A'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'AA'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'C'},\n", + " {'variant_id': 'rs13181', 'gene': 'ERCC2', 'allele': 'G'},\n", + " {'variant_id': 'rs13181', 'gene': 'ERCC2', 'allele': 'GG + GT'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'G'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'GG'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'CT'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'GG'},\n", + " {'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'}],\n", + " 'PMC4976849': [{'variant_id': 'rs1801131',\n", + " 'gene': 'MTHFR',\n", + " 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs11280056',\n", + " 'gene': 'TYMS',\n", + " 'allele': 'TTAAAGTTA/TTAAAGTTA'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'C18orf56, TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs1979277', 'gene': 'SHMT1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs3758149', 'gene': 'GGH', 'allele': 'AA'}],\n", + " 'PMC7299187': [{'variant_id': 'HLA-DRB1*07:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*07:01'}],\n", + " 'PMC4646353': [{'variant_id': 'GSTT1 non-null, GSTT1 null',\n", + " 'gene': 'GSTT1',\n", + " 'allele': 'null/null'},\n", + " {'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': 'null/null'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'AG + GG'}],\n", + " 'PMC2694861': [{'variant_id': 'rs1799752',\n", + " 'gene': 'ACE',\n", + " 'allele': 'ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC/ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC'}],\n", + " 'PMC3839910': [{'variant_id': 'HLA-B*15:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'}],\n", + " 'PMC2660379': [{'variant_id': 'rs25531', 'gene': 'SLC6A4', 'allele': 'C'},\n", + " {'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR long form (L allele)'},\n", + " {'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR long form (L allele)'}],\n", + " 'PMC5728073': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*3/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*3/*3'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'C'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GT'}],\n", + " 'PMC9585659': [{'variant_id': 'rs1799762',\n", + " 'gene': 'SERPINE1',\n", + " 'allele': 'GG'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'}],\n", + " 'PMC3164878': [{'variant_id': 'rs648893', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs671531', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs548646', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs548646', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs9322447', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs609148', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs609148', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs3823010', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs495491', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1381376', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs2075572', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs511435', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs511435', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs1381376', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs648893', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs524731', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs3778156', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC4250881': [{'variant_id': 'NAT2*4', 'gene': 'NAT2', 'allele': '*4'},\n", + " {'variant_id': 'NAT2*6', 'gene': 'NAT2', 'allele': '*6'},\n", + " {'variant_id': 'NAT2*6, NAT2*7', 'gene': 'NAT2', 'allele': '*6/*7'}],\n", + " 'PMC5089920': [{'variant_id': 'rs4880', 'gene': 'SOD2', 'allele': 'GG'}],\n", + " 'PMC10026301': [{'variant_id': 'GSTT1 null', 'gene': 'GSTT1', 'allele': None},\n", + " {'variant_id': 'GSTM1 null', 'gene': 'GSTM1', 'allele': None},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs532545', 'gene': 'CDA', 'allele': 'CC'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'}],\n", + " 'PMC3678950': [{'variant_id': 'rs61767072',\n", + " 'gene': 'ADRA2C',\n", + " 'allele': 'GGGGCGGGGCCG/GGGGCGGGGCCG'},\n", + " {'variant_id': 'rs61767072',\n", + " 'gene': 'ADRA2C',\n", + " 'allele': 'GGGGCGGGGCCG/GGGGCGGGGCCG'},\n", + " {'variant_id': 'rs1042713', 'gene': 'ADRB2', 'allele': 'GG'},\n", + " {'variant_id': 'rs1042713', 'gene': 'ADRB2', 'allele': 'G'},\n", + " {'variant_id': 'rs2230345', 'gene': 'GRK5', 'allele': 'A'},\n", + " {'variant_id': 'rs1801253', 'gene': 'ADRB1', 'allele': 'G'},\n", + " {'variant_id': 'rs2230345', 'gene': 'GRK5', 'allele': 'AT + TT'}],\n", + " 'PMC3445665': [{'variant_id': 'rs7001034', 'gene': 'FZD3', 'allele': 'A'},\n", + " {'variant_id': 'rs10771973', 'gene': 'FGD4', 'allele': 'A'},\n", + " {'variant_id': 'rs7349683', 'gene': 'EPHA5', 'allele': 'T'}],\n", + " 'PMC3680019': [{'variant_id': 'CYP2B6*1, CYP2B6*6',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*6'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*11', 'gene': 'CYP2B6', 'allele': '*1'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*9', 'gene': 'CYP2A6', 'allele': '*1'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*17', 'gene': 'CYP2A6', 'allele': '*1'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*6', 'gene': 'CYP2B6', 'allele': '*1'},\n", + " {'variant_id': 'rs3003596', 'gene': 'NR1I3', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'G'}],\n", + " 'PMC3694243': [{'variant_id': 'rs11280056',\n", + " 'gene': 'TYMS',\n", + " 'allele': 'TTA/TTA'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'G'},\n", + " {'variant_id': 'rs602950', 'gene': 'CDA', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs602950', 'gene': 'CDA', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'A'},\n", + " {'variant_id': 'rs2072671', 'gene': 'CDA', 'allele': 'A'},\n", + " {'variant_id': 'rs3215400', 'gene': 'CDA', 'allele': 'del'},\n", + " {'variant_id': 'rs602950', 'gene': 'CDA', 'allele': 'A'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'GG'},\n", + " {'variant_id': 'rs532545', 'gene': 'CDA', 'allele': 'C'},\n", + " {'variant_id': 'rs75017182', 'gene': 'DPYD', 'allele': 'CG'},\n", + " {'variant_id': 'rs2669429', 'gene': 'DPYS', 'allele': 'A'},\n", + " {'variant_id': 'rs55886062', 'gene': 'DPYD', 'allele': 'AC'},\n", + " {'variant_id': 'rs1801158', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs2297595', 'gene': 'DPYD', 'allele': 'C'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC9363274': [{'variant_id': 'HLA-DRB1*04:02',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*04:02'}],\n", + " 'PMC3619141': [{'variant_id': 'SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR short form (S allele)/HTTLPR short form (S allele)'},\n", + " {'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR short form (S allele)'},\n", + " {'variant_id': 'rs1042713', 'gene': 'ADRB2', 'allele': 'G'},\n", + " {'variant_id': 'rs6280', 'gene': 'DRD3', 'allele': 'C'},\n", + " {'variant_id': 'rs6280', 'gene': 'DRD3', 'allele': 'C'}],\n", + " 'PMC1851378': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4'},\n", + " {'variant_id': 'UGT2B15*1, UGT2B15*2', 'gene': 'UGT2B15', 'allele': '*2'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*3/*3'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'rs1042028', 'gene': 'SULT1A1', 'allele': 'CC'},\n", + " {'variant_id': 'rs3892097', 'gene': 'CYP2D6', 'allele': 'TT'}],\n", + " 'PMC3773276': [{'variant_id': 'CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'},\n", + " {'variant_id': 'CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'}],\n", + " 'PMC4872305': [{'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*1xN, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*10x2, CYP2D6*14, CYP2D6*21, CYP2D6*36, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4 + *1/*5 + *1/*10 + *1/*10x2 + *10/*10x2 + *1/*14 + *1/*21 + *1/*36 + *1/*41 + *1xN/*10 + *5/*5 + *5/*10 + *5/*21 + *5/*41 + *10/*10 + *10/*21 + *10/*36 + *10/*41'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs3213619', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'G'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs3740065', 'gene': 'ABCC2', 'allele': 'AA'},\n", + " {'variant_id': 'rs3740065', 'gene': 'ABCC2', 'allele': 'AA'}],\n", + " 'PMC8174577': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs10494366', 'gene': 'NOS1AP', 'allele': 'T'},\n", + " {'variant_id': 'rs2237892', 'gene': 'KCNQ1', 'allele': 'T'},\n", + " {'variant_id': 'rs1057868', 'gene': 'POR', 'allele': 'T'},\n", + " {'variant_id': 'rs2237895', 'gene': 'KCNQ1', 'allele': 'C'},\n", + " {'variant_id': 'rs163184', 'gene': 'KCNQ1', 'allele': 'G'},\n", + " {'variant_id': 'rs7756992', 'gene': 'CDKAL1', 'allele': 'G'},\n", + " {'variant_id': 'rs2943641', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs1801278', 'gene': 'IRS1', 'allele': 'T'},\n", + " {'variant_id': 'rs1799859', 'gene': 'ABCC8', 'allele': 'T'},\n", + " {'variant_id': 'rs1799854', 'gene': 'ABCC8', 'allele': 'A'},\n", + " {'variant_id': 'rs5219', 'gene': 'ABCC8, KCNJ11', 'allele': 'C'},\n", + " {'variant_id': 'rs757110', 'gene': 'ABCC8, KCNJ11', 'allele': 'A'},\n", + " {'variant_id': 'rs7903146', 'gene': 'TCF7L2', 'allele': 'T'}],\n", + " 'PMC9857512': [{'variant_id': 'rs12283870',\n", + " 'gene': 'CPT1A, IGHMBP2',\n", + " 'allele': 'T'},\n", + " {'variant_id': 'rs738409', 'gene': 'PNPLA3', 'allele': 'G'},\n", + " {'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'C'},\n", + " {'variant_id': 'rs887829', 'gene': 'UGT1A1', 'allele': 'T'}],\n", + " 'PMC6518412': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None}],\n", + " 'PMC5348437': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'}],\n", + " 'PMC11158376': [{'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'}],\n", + " 'PMC3883563': [{'variant_id': 'rs4646316', 'gene': 'COMT', 'allele': 'T'},\n", + " {'variant_id': 'rs9332377', 'gene': 'COMT', 'allele': 'T'},\n", + " {'variant_id': 'rs1800460', 'gene': 'TPMT', 'allele': 'T'},\n", + " {'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'C'},\n", + " {'variant_id': 'rs12201199', 'gene': 'TPMT', 'allele': 'T'}],\n", + " 'PMC2754955': [{'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'AA'},\n", + " {'variant_id': 'rs70991108', 'gene': 'DHFR', 'allele': 'del'}],\n", + " 'PMC4139556': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC1689719': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like'},\n", + " {'variant_id': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like'},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like'},\n", + " {'variant_id': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like'},\n", + " {'variant_id': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like'},\n", + " {'variant_id': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like'},\n", + " {'variant_id': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like'},\n", + " {'variant_id': 'G6PD Canton, Taiwan-Hakka, Gifu-like, Agrigento-like',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'Canton, Taiwan-Hakka, Gifu-like, Agrigento-like'}],\n", + " 'PMC4737107': [{'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C, TPMT*9, TPMT*21, TPMT*33, TPMT*34',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*2 + *1/*3A + *1/*3C + *1/*9 + *1/*21 + *1/*33 + *1/*34'},\n", + " {'variant_id': 'TPMT*1, TPMT*3C', 'gene': 'TPMT', 'allele': '*1/*3C'},\n", + " {'variant_id': 'TPMT*1, TPMT*3A', 'gene': 'TPMT', 'allele': '*1/*3A'},\n", + " {'variant_id': 'TPMT*1, TPMT*3A', 'gene': 'TPMT', 'allele': '*1/*3A'},\n", + " {'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C, TPMT*9, TPMT*21, TPMT*33, TPMT*34',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*2 + *1/*3A + *1/*3C + *1/*9 + *1/*21 + *1/*33 + *1/*34'},\n", + " {'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C, TPMT*9, TPMT*21, TPMT*32, TPMT*33, TPMT*34',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*3C'},\n", + " {'variant_id': 'TPMT*1, TPMT*3C', 'gene': 'TPMT', 'allele': '*1/*3C'},\n", + " {'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C, TPMT*9, TPMT*21, TPMT*32, TPMT*33, TPMT*34',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*2 + *1/*3A + *1/*3C + *1/*9 + *1/*21 + *1/*32 + *1/*33 + *1/*34'}],\n", + " 'PMC10605279': [{'variant_id': 'rs2768759', 'gene': 'PEAR1', 'allele': 'AC'},\n", + " {'variant_id': 'rs12041331', 'gene': 'PEAR1', 'allele': 'AA + AG'}],\n", + " 'PMC4457100': [{'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3'}],\n", + " 'PMC6132901': [{'variant_id': 'rs116855232',\n", + " 'gene': 'NUDT15',\n", + " 'allele': 'TT'},\n", + " {'variant_id': 'NUDT15*1, NUDT15*2, NUDT15*3, NUDT15*4, NUDT15*5, NUDT15*6',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*2 + *3 + *4 + *5 + *6'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'T'}],\n", + " 'PMC8191649': [{'variant_id': 'HLA-B*15:21',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:21'},\n", + " {'variant_id': 'HLA-A*24:07', 'gene': 'HLA-A', 'allele': '*24:07'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'}],\n", + " 'PMC3428903': [{'variant_id': 'HLA-A*03:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*03:01'},\n", + " {'variant_id': 'HLA-A*03:01', 'gene': 'HLA-A', 'allele': '*03:01'}],\n", + " 'PMC6817890': [{'variant_id': 'HLA-A*24:02',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*24:02'},\n", + " {'variant_id': 'HLA-B*13:01', 'gene': 'HLA-B', 'allele': '*13:01'},\n", + " {'variant_id': 'HLA-A*02:06', 'gene': 'HLA-A', 'allele': '*02:06'},\n", + " {'variant_id': 'HLA-B*44:03', 'gene': 'HLA-B', 'allele': '*44:03'},\n", + " {'variant_id': 'HLA-C*14:03', 'gene': 'HLA-C', 'allele': '*14:03'},\n", + " {'variant_id': 'HLA-DRB1*08:03', 'gene': 'HLA-DRB1', 'allele': '*08:03'},\n", + " {'variant_id': 'HLA-DRB1*12:02', 'gene': 'HLA-DRB1', 'allele': '*12:02'}],\n", + " 'PMC6612297': [{'variant_id': 'HLA-A*32:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*32:01'}],\n", + " 'PMC8422715': [{'variant_id': 'HLA-B*15:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02'}],\n", + " 'PMC4415182': [{'variant_id': 'HLA-B*15:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*46:01', 'gene': 'HLA-B', 'allele': '*46:01'}],\n", + " 'PMC2561120': [{'variant_id': 'rs7270101',\n", + " 'gene': 'ITPA',\n", + " 'allele': 'AC + CC'},\n", + " {'variant_id': 'TPMT*1, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*3A + *3C'},\n", + " {'variant_id': 'rs7270101', 'gene': 'ITPA', 'allele': 'AC + CC'}],\n", + " 'PMC10091789': [{'variant_id': 'rs10898815',\n", + " 'gene': 'NUMA1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'TT'},\n", + " {'variant_id': 'rs877087', 'gene': 'RYR3', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs877087', 'gene': 'RYR3', 'allele': 'TT'},\n", + " {'variant_id': 'rs1048101', 'gene': 'ADRA1A', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs564991', 'gene': 'APCDD1', 'allele': 'CC'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'TT'}],\n", + " 'PMC3190469': [{'variant_id': 'HLA-B*15:18, HLA-B*40:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:18/*40:01'}],\n", + " 'PMC2072824': [{'variant_id': 'HLA-B*44:02, HLA-B*44:03',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*44:02 + *44:03'},\n", + " {'variant_id': 'HLA-B*13:01', 'gene': 'HLA-B', 'allele': '*13:01'}],\n", + " 'PMC384715': [{'variant_id': 'HLA-B*57:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*57:01'}],\n", + " 'PMC6989248': [{'variant_id': 'HLA-A*02:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*02:01'},\n", + " {'variant_id': 'HLA-B*51:01', 'gene': 'HLA-B', 'allele': '*51:01'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'},\n", + " {'variant_id': 'HLA-C*03:04', 'gene': 'HLA-C', 'allele': '*03:04'}],\n", + " 'PMC3616517': [{'variant_id': 'HLA-DQB1*05:01',\n", + " 'gene': 'HLA-DQB1',\n", + " 'allele': '*05:01'},\n", + " {'variant_id': 'HLA-DQB1*06:02', 'gene': 'HLA-DQB1', 'allele': '*06:02'},\n", + " {'variant_id': 'HLA-DQB1*06:03', 'gene': 'HLA-DQB1', 'allele': '*06:03'},\n", + " {'variant_id': 'HLA-DQB1*06:02', 'gene': 'HLA-DQB1', 'allele': '*06:02'},\n", + " {'variant_id': 'HLA-DRB1*15:03', 'gene': 'HLA-DRB1', 'allele': '*15:03'},\n", + " {'variant_id': 'HLA-DQB1*06:09', 'gene': 'HLA-DQB1', 'allele': '*06:09'},\n", + " {'variant_id': 'HLA-DQB1*02:01', 'gene': 'HLA-DQB1', 'allele': '*02:01'},\n", + " {'variant_id': 'HLA-DQB1*02:01', 'gene': 'HLA-DQB1', 'allele': '*02:01'},\n", + " {'variant_id': 'HLA-DQB1*03:02', 'gene': 'HLA-DQB1', 'allele': '*03:02'},\n", + " {'variant_id': 'HLA-DQB1*06:02', 'gene': 'HLA-DQB1', 'allele': '*06:02'},\n", + " {'variant_id': 'HLA-DQB1*03:02', 'gene': 'HLA-DQB1', 'allele': '*03:02'},\n", + " {'variant_id': 'HLA-C*04:01', 'gene': 'HLA-C', 'allele': '*04:01'},\n", + " {'variant_id': 'HLA-C*04:01', 'gene': 'HLA-C', 'allele': '*04:01'},\n", + " {'variant_id': 'HLA-DQB1*05:01', 'gene': 'HLA-DQB1', 'allele': '*05:01'}],\n", + " 'PMC6051654': [{'variant_id': 'HLA-DRB1*01:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*01:01'},\n", + " {'variant_id': 'HLA-A*11:01', 'gene': 'HLA-A', 'allele': '*11:01'},\n", + " {'variant_id': 'HLA-DQB1*06:02', 'gene': 'HLA-DQB1', 'allele': '*06:02'},\n", + " {'variant_id': 'HLA-DQB1*03:03', 'gene': 'HLA-DQB1', 'allele': '*03:03'}],\n", + " 'PMC7674263': [{'variant_id': 'HLA-A*32:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*32:01'}],\n", + " 'PMC3551216': [{'variant_id': 'HLA-DRB1*01:02',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*01:02'},\n", + " {'variant_id': 'HLA-B*58:01', 'gene': 'HLA-B', 'allele': '*58:01'},\n", + " {'variant_id': 'HLA-B*58:02', 'gene': 'HLA-B', 'allele': '*58:02'},\n", + " {'variant_id': 'HLA-C*02:10', 'gene': 'HLA-C', 'allele': '*02:10'},\n", + " {'variant_id': 'HLA-B*15:10', 'gene': 'HLA-B', 'allele': '*15:10'}],\n", + " 'PMC4141516': [{'variant_id': 'HLA-DRB1*07:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*07:01'}],\n", + " 'PMC4819766': [{'variant_id': 'HLA-DRB1*07:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*07:01'}],\n", + " 'PMC3487790': [{'variant_id': 'rs4969170', 'gene': None, 'allele': 'AA'},\n", + " {'variant_id': 'rs2660', 'gene': 'OAS1', 'allele': 'AG'},\n", + " {'variant_id': 'rs231775', 'gene': 'CTLA4', 'allele': 'AG'},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'AC'},\n", + " {'variant_id': 'rs231775', 'gene': 'CTLA4', 'allele': 'A'},\n", + " {'variant_id': 'rs4969170', 'gene': 'SOCS3', 'allele': 'AG'},\n", + " {'variant_id': 'rs8099917', 'gene': 'IFNL3', 'allele': 'T'}],\n", + " 'PMC3049596': [{'variant_id': 'CYP2A6*1, CYP2A6*4, CYP2A6*7, CYP2A6*9, CYP2A6*10',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*4, CYP2A6*7, CYP2A6*9, CYP2A6*10',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*1'}],\n", + " 'PMC5634615': [{'variant_id': 'HLA-B*35:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*35:02'}],\n", + " 'PMC2709795': [{'variant_id': 'CYP2C8*1, CYP2C8*3',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': '*3'},\n", + " {'variant_id': 'CYP2C8*1, CYP2C8*3', 'gene': 'CYP2C8', 'allele': '*3'},\n", + " {'variant_id': 'CYP2C8*1, CYP2C8*3', 'gene': 'CYP2C8', 'allele': '*3'}],\n", + " 'PMC4420567': [{'variant_id': 'CYP2D6*1, CYP2D6*5',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*5'}],\n", + " 'PMC8263746': [{'variant_id': 'NUDT15*1, NUDT15*2, NUDT15*3, NUDT15*5',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*2/*2 +*2/*3 +*3/*3 + *5/*5'},\n", + " {'variant_id': 'NUDT15*1, NUDT15*2, NUDT15*3, NUDT15*4, NUDT15*5',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*2/*2 +*2/*3 +*3/*3 + *5/*5'}],\n", + " 'PMC8742639': [{'variant_id': 'SLCO1B1*15',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*15'}],\n", + " 'PMC3909953': [{'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4 + *5 + *10 + *41'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4 + *5 + *10 + *41'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4 + *1/*10 + *1/*5 + *1/*41 + *1/*3'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*5',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*4 + *3/*5 + *4/*4 + *4/*5'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4 + *5 + *10 + *41'},\n", + " {'variant_id': 'rs3892097', 'gene': 'CYP2D6', 'allele': 'T'}],\n", + " 'PMC8441053': [{'variant_id': 'CYP2D6*1, CYP2D6*4, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*1 + *10/*10 + *1/*4 + *1/*10'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*17 + *17/*17'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'}],\n", + " 'PMC9924616': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC4110085': [{'variant_id': 'rs1801133',\n", + " 'gene': 'MTHFR',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs2236225', 'gene': 'MTHFD1', 'allele': 'A'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'G'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'AA + AG'}],\n", + " 'PMC1895006': [{'variant_id': 'rs45445694',\n", + " 'gene': 'C18orf56, TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3'}],\n", + " 'PMC7929788': [{'variant_id': 'rs1799752',\n", + " 'gene': 'ACE',\n", + " 'allele': 'del/del'}],\n", + " 'PMC8409603': [{'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *3/*3'}],\n", + " 'PMC8790808': [{'variant_id': 'HLA-DQA1*02:01',\n", + " 'gene': 'HLA-DQA1',\n", + " 'allele': '*02:01'},\n", + " {'variant_id': 'HLA-DQB1*02:02', 'gene': 'HLA-DQB1', 'allele': '*02:02'},\n", + " {'variant_id': 'HLA-DRB1*07:01', 'gene': 'HLA-DRB1', 'allele': '*07:01'},\n", + " {'variant_id': 'rs9958628', 'gene': 'ARHGAP28', 'allele': 'T'}],\n", + " 'PMC8415730': [{'variant_id': 'rs1799752', 'gene': 'ACE', 'allele': 'del'},\n", + " {'variant_id': 'rs2285666', 'gene': 'ACE2', 'allele': 'C'}],\n", + " 'PMC8426884': [{'variant_id': 'rs1799752', 'gene': 'ACE', 'allele': 'del'},\n", + " {'variant_id': 'rs2106809', 'gene': 'ACE2', 'allele': 'A'},\n", + " {'variant_id': 'rs2285666', 'gene': 'ACE2', 'allele': 'C'}],\n", + " 'PMC8596349': [{'variant_id': 'rs1799752',\n", + " 'gene': 'ACE',\n", + " 'allele': 'ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC'},\n", + " {'variant_id': 'rs699', 'gene': 'AGT', 'allele': 'AG'}],\n", + " 'PMC8166347': [{'variant_id': 'rs1799752',\n", + " 'gene': 'ACE',\n", + " 'allele': 'ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC/ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC'},\n", + " {'variant_id': 'rs2074192', 'gene': 'ACE2', 'allele': 'T'},\n", + " {'variant_id': 'rs2106809', 'gene': 'ACE2', 'allele': 'A'},\n", + " {'variant_id': 'rs699', 'gene': 'AGT', 'allele': 'A'},\n", + " {'variant_id': 'rs5186', 'gene': 'AGTR1', 'allele': 'C'},\n", + " {'variant_id': 'rs4762', 'gene': 'AGT', 'allele': 'A'}],\n", + " 'PMC9925376': [{'variant_id': 'CYP2C19*1, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*17/*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *2/*1 + *2/*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *2/*1 + *2/*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *2/*1 + *2/*17'}],\n", + " 'PMC8621157': [{'variant_id': 'rs1799752', 'gene': 'ACE', 'allele': 'del'},\n", + " {'variant_id': 'rs1799752', 'gene': 'ACE', 'allele': 'del/del'},\n", + " {'variant_id': 'rs4240157', 'gene': 'ACE2', 'allele': 'CC'},\n", + " {'variant_id': 'rs4240157', 'gene': 'ACE2', 'allele': 'C'}],\n", + " 'PMC9974434': [{'variant_id': 'NUDT15*1, NUDT15*2, NUDT15*3, NUDT15*5, NUDT15*6',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*1/*2 + *1/*3 + *1/*5 + *1/*6'},\n", + " {'variant_id': 'NUDT15*1, NUDT15*2, NUDT15*3, NUDT15*5, NUDT15*6',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*1/*2 + *1/*3 + *1/*5 + *1/*6'}],\n", + " 'PMC3580556': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*17 + *17/*17'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GT'},\n", + " {'variant_id': 'rs762551', 'gene': 'CYP1A2', 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs3213422', 'gene': 'DHODH', 'allele': 'AA'}],\n", + " 'PMC7308427': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'TT'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3 + (CCGCGCCACTTGGCCTGCCTCCGTCCCG)3/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'TT'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'A'}],\n", + " 'PMC10564446': [{'variant_id': 'HLA-DRB1*15:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*15:01'}],\n", + " 'PMC2816343': [{'variant_id': 'CYP2D6*5, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*5/*10'},\n", + " {'variant_id': 'CYP2D6*5, CYP2D6*10', 'gene': 'CYP2D6', 'allele': '*5/*10'}],\n", + " 'PMC9442161': [{'variant_id': 'GSTT1 non-null, GSTT1 null',\n", + " 'gene': 'GSTT1',\n", + " 'allele': None},\n", + " {'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': None}],\n", + " 'PMC6145764': [{'variant_id': 'HLA-A*31:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*31:01'}],\n", + " 'PMC2714288': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)'},\n", + " {'variant_id': 'rs6311', 'gene': 'HTR2A', 'allele': 'T'}],\n", + " 'PMC1770668': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC3939416': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*3 + *1/*1'}],\n", + " 'PMC9990631': [{'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'CT + TT'}],\n", + " 'PMC10309546': [{'variant_id': 'rs11280056', 'gene': 'TYMS', 'allele': 'TTA'},\n", + " {'variant_id': 'rs648743', 'gene': 'CTH', 'allele': 'CC'},\n", + " {'variant_id': 'rs2853539', 'gene': 'TYMS', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1937840', 'gene': 'AKR1C3', 'allele': 'G'},\n", + " {'variant_id': 'rs1544105', 'gene': 'FPGS', 'allele': 'TT'},\n", + " {'variant_id': 'rs3754446', 'gene': 'GSTM5', 'allele': 'C'},\n", + " {'variant_id': 'rs17343066', 'gene': 'SLC28A3', 'allele': 'AA'},\n", + " {'variant_id': 'rs12067645', 'gene': 'DCTD', 'allele': 'AA'},\n", + " {'variant_id': 'rs12067645', 'gene': 'CTPS1', 'allele': 'A'},\n", + " {'variant_id': 'rs12067645', 'gene': 'CTPS1', 'allele': 'A'},\n", + " {'variant_id': 'rs4148737', 'gene': 'ABCB1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'T'}],\n", + " 'PMC9298338': [{'variant_id': 'rs72552763',\n", + " 'gene': 'SLC22A1',\n", + " 'allele': 'del'},\n", + " {'variant_id': 'rs72552763', 'gene': 'SLC22A1', 'allele': 'del'},\n", + " {'variant_id': 'rs34059508', 'gene': 'SLC22A1', 'allele': 'A'},\n", + " {'variant_id': 'rs12208357', 'gene': 'SLC22A1', 'allele': 'T'},\n", + " {'variant_id': 'rs34059508', 'gene': 'SLC22A1', 'allele': 'A'},\n", + " {'variant_id': 'rs12208357', 'gene': 'SLC22A1', 'allele': 'T'},\n", + " {'variant_id': 'rs34130495', 'gene': 'SLC22A1', 'allele': 'A'},\n", + " {'variant_id': 'rs34130495', 'gene': 'SLC22A1', 'allele': 'A'},\n", + " {'variant_id': 'rs72552763', 'gene': 'SLC22A1', 'allele': 'del'},\n", + " {'variant_id': 'rs12208357', 'gene': 'SLC22A1', 'allele': 'T'},\n", + " {'variant_id': 'rs34059508', 'gene': 'SLC22A1', 'allele': 'A'},\n", + " {'variant_id': 'rs34130495', 'gene': 'SLC22A1', 'allele': 'A'}],\n", + " 'PMC10189794': [{'variant_id': 'HLA-DRB1*07:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*07:01'},\n", + " {'variant_id': 'HLA-DRB1*12:02', 'gene': 'HLA-DRB1', 'allele': '*12:02'},\n", + " {'variant_id': 'HLA-DRB1*04:05', 'gene': 'HLA-DRB1', 'allele': '*04:05'},\n", + " {'variant_id': 'HLA-DRB1*07:01', 'gene': 'HLA-DRB1', 'allele': '*07:01'}],\n", + " 'PMC9806075': [{'variant_id': 'HLA-C*01:02',\n", + " 'gene': 'HLA-C',\n", + " 'allele': '*01:02'}],\n", + " 'PMC2492911': [{'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'AA'}],\n", + " 'PMC8097083': [{'variant_id': 'rs383510', 'gene': 'TMPRSS2', 'allele': 'CC'},\n", + " {'variant_id': 'rs12329760', 'gene': 'TMPRSS2', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2070788', 'gene': 'TMPRSS2', 'allele': 'GG'}],\n", + " 'PMC3537445': [{'variant_id': 'UGT1A1*1, UGT1A1*28, UGT1A1*36',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28, UGT1A1*36',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28, UGT1A1*36',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'}],\n", + " 'PMC3946470': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'}],\n", + " 'PMC10411392': [{'variant_id': 'POR deficiency',\n", + " 'gene': 'POR',\n", + " 'allele': None}],\n", + " 'PMC6631360': [{'variant_id': 'rs1402467', 'gene': 'SULT1C4', 'allele': 'G'},\n", + " {'variant_id': 'rs6922548', 'gene': 'PPARD', 'allele': 'G'},\n", + " {'variant_id': 'rs7769719', 'gene': 'PPARD', 'allele': 'G'},\n", + " {'variant_id': 'rs2016520', 'gene': 'PPARD', 'allele': 'C'},\n", + " {'variant_id': 'rs1871450', 'gene': 'CHST3', 'allele': 'A'},\n", + " {'variant_id': 'rs1883322', 'gene': 'PPARD', 'allele': 'C'},\n", + " {'variant_id': 'rs12418', 'gene': 'CHST3', 'allele': 'A'},\n", + " {'variant_id': 'rs4148943', 'gene': 'CHST3', 'allele': 'C'},\n", + " {'variant_id': 'rs4148945', 'gene': 'CHST3', 'allele': 'C'},\n", + " {'variant_id': 'rs3734254', 'gene': 'PPARD', 'allele': 'T'},\n", + " {'variant_id': 'rs4148947', 'gene': 'CHST3', 'allele': 'T'},\n", + " {'variant_id': 'rs4148950', 'gene': 'CHST3', 'allele': 'A'},\n", + " {'variant_id': 'rs730720', 'gene': 'CHST3', 'allele': 'C'},\n", + " {'variant_id': 'rs12960', 'gene': 'RPL13, SNORD68, SPG7', 'allele': 'G'},\n", + " {'variant_id': 'rs1871450', 'gene': 'CHST3', 'allele': 'A'},\n", + " {'variant_id': 'rs1799931', 'gene': 'NAT2', 'allele': 'G'},\n", + " {'variant_id': 'rs4646487', 'gene': 'CYP4B1', 'allele': 'T'},\n", + " {'variant_id': 'rs2292954', 'gene': 'SPG7', 'allele': 'A'},\n", + " {'variant_id': 'rs2238472', 'gene': 'ABCC6', 'allele': 'C'},\n", + " {'variant_id': 'rs2227291', 'gene': 'ATP7A', 'allele': 'G'},\n", + " {'variant_id': 'rs4148945', 'gene': 'CHST3', 'allele': 'C'},\n", + " {'variant_id': 'rs2301159', 'gene': 'SLC10A2', 'allele': 'A'},\n", + " {'variant_id': 'rs4148950', 'gene': 'CHST3', 'allele': 'A'}],\n", + " 'PMC3513412': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR long form (L allele)'},\n", + " {'variant_id': 'rs1800795', 'gene': 'IL6', 'allele': 'CC'}],\n", + " 'PMC8841796': [{'variant_id': 'CYP3A4*22',\n", + " 'gene': 'CYP3A4',\n", + " 'allele': '*22/*22'}],\n", + " 'PMC10272067': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3 + *2/*17 + *1/*17'}],\n", + " 'PMC3279131': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)'}],\n", + " 'PMC6313513': [{'variant_id': 'rs6935207', 'gene': 'SLC22A1', 'allele': 'A'},\n", + " {'variant_id': 'rs6935207', 'gene': 'SLC22A1', 'allele': 'A'},\n", + " {'variant_id': 'rs6935207', 'gene': 'SLC22A1', 'allele': 'A'}],\n", + " 'PMC5766375': [{'variant_id': 'CYP3A5*1, CYP3A5*3, CYP3A5*6, CYP3A5*7',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3 + *1/*6 + *1/*7'}],\n", + " 'PMC8833506': [{'variant_id': 'CYP3A5*1, CYP3A5*3, CYP3A5*6, CYP3A5*7',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3 + *1/*6 + *1/*7'}],\n", + " 'PMC9125581': [{'variant_id': 'HLA-B*07:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*07:02'},\n", + " {'variant_id': 'HLA-C*07:02', 'gene': 'HLA-C', 'allele': '*07:02'}],\n", + " 'PMC8909027': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*6 + *1/*28 + *6/*28 + *6/*6 + *28/*28'}],\n", + " 'PMC3189112': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'}],\n", + " 'PMC3740059': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3'}],\n", + " 'PMC4921119': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*3/*3'}],\n", + " 'PMC10214954': [{'variant_id': 'TPMT*3C', 'gene': 'TPMT', 'allele': '*3C'},\n", + " {'variant_id': 'rs2281547', 'gene': 'XDH', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'AC'},\n", + " {'variant_id': 'rs2281547', 'gene': 'XDH', 'allele': 'CC + CT'}],\n", + " 'PMC10244018': [{'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'}],\n", + " 'PMC8917764': [{'variant_id': 'CYP2D6 poor metabolizers and intermediate metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizers and intermediate metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC10288459': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4'}],\n", + " 'PMC10298094': [{'variant_id': 'CYP2C8*1, CYP2C8*2, CYP2C8*3, CYP2C8*4',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': '*2 + *3 + *4'},\n", + " {'variant_id': 'DPYD Reference', 'gene': 'DPYD', 'allele': 'Reference'}],\n", + " 'PMC8376784': [{'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC'}],\n", + " 'PMC5972163': [{'variant_id': 'CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'}],\n", + " 'PMC8602035': [{'variant_id': 'HLA-B*15:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*56:02', 'gene': 'HLA-B', 'allele': '*56:02'},\n", + " {'variant_id': 'HLA-B*51:01', 'gene': 'HLA-B', 'allele': '*51:01'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'HLA-B*08:01', 'gene': 'HLA-B', 'allele': '*08:01'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'HLA-B*13:01', 'gene': 'HLA-B', 'allele': '*13:01'},\n", + " {'variant_id': 'HLA-B*15:21', 'gene': 'HLA-B', 'allele': '*15:21'}],\n", + " 'PMC10582655': [{'variant_id': 'HLA-C*01:02',\n", + " 'gene': 'HLA-C',\n", + " 'allele': '*01:02'},\n", + " {'variant_id': 'HLA-B*46:01', 'gene': 'HLA-B', 'allele': '*46:01'}],\n", + " 'PMC3417133': [{'variant_id': 'SLCO1B1*1, SLCO1B1*5',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*5'},\n", + " {'variant_id': 'SLCO1B1*1, SLCO1B1*5', 'gene': 'SLCO1B1', 'allele': '*5'}],\n", + " 'PMC3028503': [{'variant_id': 'rs2230345',\n", + " 'gene': 'GRK5',\n", + " 'allele': 'AT + TT'}],\n", + " 'PMC11042465': [{'variant_id': 'SLCO1B1*1, SLCO1B1*5',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*1/*5'}],\n", + " 'PMC4574842': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'},\n", + " {'variant_id': 'rs75017182', 'gene': 'DPYD', 'allele': 'CG'},\n", + " {'variant_id': 'rs17376848', 'gene': 'DPYD', 'allele': 'AG'},\n", + " {'variant_id': 'rs895819', 'gene': 'MIR27A', 'allele': 'CC'},\n", + " {'variant_id': 'rs2297595', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC3597465': [{'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'GG'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'GG'},\n", + " {'variant_id': 'rs6265', 'gene': 'BDNF', 'allele': 'C'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs6265', 'gene': 'BDNF', 'allele': 'CC'},\n", + " {'variant_id': 'rs6265', 'gene': 'BDNF', 'allele': 'C'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'A'}],\n", + " 'PMC4729652': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28'},\n", + " {'variant_id': 'rs2306283', 'gene': 'SLCO1B1', 'allele': 'GG'},\n", + " {'variant_id': 'rs10929302', 'gene': 'UGT1A1', 'allele': 'AA'}],\n", + " 'PMC3750958': [{'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'TT'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'TT'}],\n", + " 'PMC3358736': [{'variant_id': 'rs2717162',\n", + " 'gene': 'GALR1',\n", + " 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2717162', 'gene': 'GALR1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2717162', 'gene': 'GALR1', 'allele': 'CC + CT'}],\n", + " 'PMC10350251': [{'variant_id': 'NUDT15*2, NUDT15*3, NUDT15*5, NUDT15*6',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*2 + *3 + *5 + *6'},\n", + " {'variant_id': 'NUDT15*2, NUDT15*3', 'gene': 'NUDT15', 'allele': '*2/*3'},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'AC'}],\n", + " 'PMC2954689': [{'variant_id': 'HLA-DQB1*06:02',\n", + " 'gene': 'HLA-DQB1',\n", + " 'allele': '*06:02'}],\n", + " 'PMC3248605': [{'variant_id': 'HLA-DQB1*06:02',\n", + " 'gene': 'HLA-DQB1',\n", + " 'allele': '*06:02'}],\n", + " 'PMC10361978': [{'variant_id': 'CYP2B6*1, CYP2B6*4, CYP2B6*5, CYP2B6*6, CYP2B6*22',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*1/*6 + *6/*6 + *4/*6 + *5/*6'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*4, CYP2B6*5, CYP2B6*6, CYP2B6*22',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*1/*1 + *1/*5 + *1/*4 + *1/*22'}],\n", + " 'PMC10352989': [{'variant_id': 'poor metabolizer',\n", + " 'gene': None,\n", + " 'allele': None},\n", + " {'variant_id': 'CYP3A4*1, CYP3A4*22', 'gene': 'CYP3A4', 'allele': '*22'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*3'}],\n", + " 'PMC4803136': [{'variant_id': 'rs3737967', 'gene': 'MTHFR', 'allele': 'GG'},\n", + " {'variant_id': 'rs2274976', 'gene': 'MTHFR', 'allele': 'CC'},\n", + " {'variant_id': 'rs17264736', 'gene': 'ABCC1', 'allele': 'GG + GT'},\n", + " {'variant_id': 'rs1017860', 'gene': 'NFATC1', 'allele': 'CC'},\n", + " {'variant_id': 'rs4781712', 'gene': 'ABCC1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs8090560', 'gene': 'NFATC1', 'allele': 'GG'},\n", + " {'variant_id': 'rs34965641', 'gene': 'DHFR', 'allele': 'GG'},\n", + " {'variant_id': 'rs2177735', 'gene': 'ATIC', 'allele': 'AA'},\n", + " {'variant_id': 'rs6123048', 'gene': 'NFATC2', 'allele': 'AA'},\n", + " {'variant_id': 'rs4818789', 'gene': 'SLC19A1', 'allele': 'TT'},\n", + " {'variant_id': 'rs4819128', 'gene': 'SLC19A1', 'allele': 'TT'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'CC'},\n", + " {'variant_id': 'rs17514110', 'gene': 'ATIC', 'allele': 'CC'},\n", + " {'variant_id': 'rs3787186', 'gene': 'NFATC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs3740065', 'gene': 'ABCC2', 'allele': 'AA'},\n", + " {'variant_id': 'rs8058040', 'gene': 'ABCC1', 'allele': 'AA'}],\n", + " 'PMC5859084': [{'variant_id': 'rs55633228', 'gene': 'PIK3R2', 'allele': 'T'},\n", + " {'variant_id': 'rs117101815', 'gene': 'PERP', 'allele': 'T'},\n", + " {'variant_id': 'rs3805945', 'gene': 'PPP2R5D', 'allele': 'C'},\n", + " {'variant_id': 'rs77769901', 'gene': 'CCNK', 'allele': 'G'},\n", + " {'variant_id': 'rs118129530', 'gene': 'PIK3R2', 'allele': 'A'},\n", + " {'variant_id': 'rs148013902', 'gene': 'PIK3R2', 'allele': 'T'},\n", + " {'variant_id': 'rs145623321', 'gene': 'PIK3R2', 'allele': 'T'},\n", + " {'variant_id': 'rs117458836', 'gene': 'CYP2C8', 'allele': 'A'},\n", + " {'variant_id': 'rs74743371', 'gene': 'CBR3', 'allele': 'T'},\n", + " {'variant_id': 'rs118088833', 'gene': 'TP53AIP1', 'allele': 'T'},\n", + " {'variant_id': 'rs112242273', 'gene': 'GNL3', 'allele': 'T'},\n", + " {'variant_id': 'rs181501757', 'gene': 'TOP2A', 'allele': 'A'},\n", + " {'variant_id': 'rs9402944', 'gene': 'PERP', 'allele': 'T'},\n", + " {'variant_id': 'rs112783657', 'gene': 'CBR3', 'allele': 'T'},\n", + " {'variant_id': 'rs149212925', 'gene': 'NOS1', 'allele': 'G'},\n", + " {'variant_id': 'rs58695150', 'gene': 'PIK3R2', 'allele': 'T'},\n", + " {'variant_id': 'rs150688309', 'gene': 'PIK3R2', 'allele': 'G'},\n", + " {'variant_id': 'rs299293', 'gene': 'HMMR', 'allele': 'T'},\n", + " {'variant_id': 'rs9389568', 'gene': 'PERP', 'allele': 'C'},\n", + " {'variant_id': 'rs144991623', 'gene': 'FOXO1', 'allele': 'T'},\n", + " {'variant_id': 'rs8110364', 'gene': 'PIK3R2', 'allele': 'A'},\n", + " {'variant_id': 'rs117341846', 'gene': 'PIK3R2', 'allele': 'T'},\n", + " {'variant_id': 'rs115457081', 'gene': 'IRS1', 'allele': 'A'},\n", + " {'variant_id': 'rs142244113', 'gene': 'INSR', 'allele': 'T'},\n", + " {'variant_id': 'rs79430272', 'gene': 'PIK3R2', 'allele': 'T'},\n", + " {'variant_id': 'rs4968187', 'gene': 'TP53', 'allele': 'T'},\n", + " {'variant_id': 'rs117951771', 'gene': 'PIK3R2', 'allele': 'T'},\n", + " {'variant_id': 'rs148235907', 'gene': 'PIK3R2', 'allele': 'A'},\n", + " {'variant_id': 'rs299313', 'gene': 'HMMR', 'allele': 'A'},\n", + " {'variant_id': 'rs299314', 'gene': 'HMMR', 'allele': 'C'},\n", + " {'variant_id': 'rs41412545', 'gene': 'INSR', 'allele': 'A'},\n", + " {'variant_id': 'rs56022120', 'gene': 'PIK3R2', 'allele': 'T'},\n", + " {'variant_id': 'rs138602176', 'gene': 'PIK3R2', 'allele': 'A'},\n", + " {'variant_id': 'rs78428806', 'gene': 'PERP', 'allele': 'A'},\n", + " {'variant_id': 'rs141084494', 'gene': 'RBX1', 'allele': 'A'}],\n", + " 'PMC6752748': [{'variant_id': 'rs746071566',\n", + " 'gene': 'NUDT15',\n", + " 'allele': 'GGAGTC'},\n", + " {'variant_id': 'rs746071566', 'gene': 'NUDT15', 'allele': 'del'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'T'}],\n", + " 'PMC1786217': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC4369993': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC4230293': [{'variant_id': 'CYP2D6*1, CYP2D6*4, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4 + *1/*10 + *4/*10 + *10/*10 + *4/*4'}],\n", + " 'PMC3902809': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*1 + *1/*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *3/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1 + *2 + *3 + *17'}],\n", + " 'PMC4063287': [{'variant_id': 'SLCO1B1*15, SLCO1B1*37',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*15/*15'},\n", + " {'variant_id': 'SLCO1B1*1, SLCO1B1*15, SLCO1B1*37',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*1/*15'},\n", + " {'variant_id': 'SLCO1B1*1, SLCO1B1*15',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*1/*15'},\n", + " {'variant_id': 'SLCO1B1*1, SLCO1B1*15',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*15/*15'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'T'},\n", + " {'variant_id': 'rs2231137', 'gene': 'ABCG2', 'allele': 'T'},\n", + " {'variant_id': 'rs17222723', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs8187710', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CT'}],\n", + " 'PMC4760893': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *2/*3 + *1/*3 + *2/*17'}],\n", + " 'PMC4567270': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'T'}],\n", + " 'PMC6394110': [{'variant_id': 'UGT1A1*6, UGT1A1*28, UGT1A1*37',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28 + *37 + *6'}],\n", + " 'PMC2865761': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'}],\n", + " 'PMC3261355': [{'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'CC'},\n", + " {'variant_id': 'rs61162043', 'gene': None, 'allele': 'G'}],\n", + " 'PMC4413440': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'}],\n", + " 'PMC3785800': [{'variant_id': 'UGT1A1*1, UGT1A1*28, UGT1A1*36, UGT1A1*37',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*36 + *28/*36 + *36/*37 + *1/*1'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28, UGT1A1*36, UGT1A1*37',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*36 + *28/*36 + *36/*37 + *1/*1 + *1/*28 + *1/*37 + *28/*28 + *28/*37'}],\n", + " 'PMC2683398': [{'variant_id': 'CYP2C9*1, CYP2C9*2',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*6', 'gene': 'CYP2C9', 'allele': '*6'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*11', 'gene': 'CYP2C9', 'allele': '*11'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*5', 'gene': 'CYP2C9', 'allele': '*5'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'}],\n", + " 'PMC2525499': [{'variant_id': 'rs2072661', 'gene': 'CHRNB2', 'allele': 'GG'}],\n", + " 'PMC4991479': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'G'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'A'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'A'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'G'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'}],\n", + " 'PMC2679414': [{'variant_id': 'rs3736228', 'gene': 'LRP5', 'allele': 'T'},\n", + " {'variant_id': 'rs3736228', 'gene': 'LRP5', 'allele': 'T'},\n", + " {'variant_id': 'rs4355801', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs4355801', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs3736228', 'gene': 'LRP5', 'allele': 'T'},\n", + " {'variant_id': 'rs4355801', 'gene': None, 'allele': 'G'}],\n", + " 'PMC2713584': [{'variant_id': 'rs2301149',\n", + " 'gene': 'KCNIP1, KCNMB1',\n", + " 'allele': 'C'},\n", + " {'variant_id': 'rs11739136', 'gene': 'KCNIP1, KCNMB1', 'allele': 'CT + TT'}],\n", + " 'PMC3897654': [{'variant_id': 'rs1057868', 'gene': 'POR', 'allele': 'C'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*3/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*3/*3'},\n", + " {'variant_id': 'rs2242480', 'gene': 'CYP3A4', 'allele': 'C/C'},\n", + " {'variant_id': 'rs2242480', 'gene': 'CYP3A4', 'allele': 'C/C'},\n", + " {'variant_id': 'rs4646437', 'gene': 'CYP3A4', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs165599', 'gene': 'COMT', 'allele': 'A'},\n", + " {'variant_id': 'rs1800871', 'gene': 'IL10', 'allele': 'A'},\n", + " {'variant_id': 'rs1800896', 'gene': 'IL10', 'allele': 'CT'},\n", + " {'variant_id': 'rs1800872', 'gene': 'IL10', 'allele': 'G'},\n", + " {'variant_id': 'rs2868177', 'gene': 'POR', 'allele': 'A'},\n", + " {'variant_id': 'rs4646312', 'gene': 'COMT', 'allele': 'C'},\n", + " {'variant_id': 'rs2239393', 'gene': 'COMT', 'allele': 'A'},\n", + " {'variant_id': 'rs737865', 'gene': 'COMT', 'allele': 'A'},\n", + " {'variant_id': 'rs6267', 'gene': 'COMT', 'allele': 'G'},\n", + " {'variant_id': 'rs4680', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs4646437', 'gene': 'CYP3A4', 'allele': 'GG'},\n", + " {'variant_id': 'rs1800872', 'gene': 'IL10', 'allele': 'TT'},\n", + " {'variant_id': 'rs1800871', 'gene': 'IL10', 'allele': 'AA'}],\n", + " 'PMC4936837': [{'variant_id': 'rs2279343',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs2279343', 'gene': 'CYP2B6', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'CT + TT'}],\n", + " 'PMC4716887': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*3/*3'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*3 + *3/*3'}],\n", + " 'PMC404497': [{'variant_id': 'rs1800497',\n", + " 'gene': 'ANKK1',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC6028015': [{'variant_id': 'CYP2D6 poor metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC3760448': [{'variant_id': 'rs3740065', 'gene': 'ABCC2', 'allele': 'AG'},\n", + " {'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'CT'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AG'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'C'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AG'}],\n", + " 'PMC3746507': [{'variant_id': 'rs113993960',\n", + " 'gene': 'CFTR',\n", + " 'allele': 'del/del'},\n", + " {'variant_id': 'rs113993960', 'gene': 'CFTR', 'allele': 'del/del'}],\n", + " 'PMC5372085': [{'variant_id': 'HLA-B*07:02, HLA-B*15:21',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:21'}],\n", + " 'PMC5753619': [{'variant_id': 'HLA-B*18:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*18:01'},\n", + " {'variant_id': 'HLA-B*15:21', 'gene': 'HLA-B', 'allele': '*15:21'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'}],\n", + " 'PMC4652259': [{'variant_id': 'HLA-B*59:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*59:01'}],\n", + " 'PMC5370513': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'rs4148323', 'gene': 'UGT1A1', 'allele': 'A'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*1 + *1/*28'},\n", + " {'variant_id': 'rs4148323', 'gene': 'UGT1A1', 'allele': 'A'},\n", + " {'variant_id': 'rs4148323', 'gene': 'UGT1A1', 'allele': 'A'}],\n", + " 'PMC3834081': [{'variant_id': 'CYP2A6*1, CYP2A6*9',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*9'}],\n", + " 'PMC3834031': [{'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'}],\n", + " 'PMC5298525': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'rs762551', 'gene': 'CYP1A2', 'allele': 'AA'},\n", + " {'variant_id': 'rs762551', 'gene': 'CYP1A2', 'allele': 'AA'},\n", + " {'variant_id': 'rs1137101', 'gene': 'LEPR', 'allele': 'AG + GG'}],\n", + " 'PMC2570505': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'}],\n", + " 'PMC3946972': [{'variant_id': 'CYP2A6 high activity',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2A6 high activity', 'gene': 'CYP2A6', 'allele': None},\n", + " {'variant_id': 'CYP2A6 low activity', 'gene': 'CYP2A6', 'allele': None}],\n", + " 'PMC6710506': [{'variant_id': 'HLA-B*56:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*56:02'}],\n", + " 'PMC6562829': [{'variant_id': 'CYP2D6 poor metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor and ultrarapid metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC8033847': [{'variant_id': 'rs1799752',\n", + " 'gene': 'ACE',\n", + " 'allele': 'ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC'},\n", + " {'variant_id': 'rs71103505',\n", + " 'gene': 'BDKRB2',\n", + " 'allele': '(GGTGGGGAC)2/(GGTGGGGAC)2 + (GGTGGGGAC)2/GGTGGGGACGGTGGGGACGGTGGGGAC'},\n", + " {'variant_id': 'rs1799722', 'gene': 'BDKRB2', 'allele': 'T'}],\n", + " 'PMC2869454': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*3'}],\n", + " 'PMC3710507': [{'variant_id': 'HLA-B*38:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*38:02'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'}],\n", + " 'PMC5600660': [{'variant_id': 'rs1799752', 'gene': 'ACE', 'allele': 'del'},\n", + " {'variant_id': 'rs7412', 'gene': 'APOE', 'allele': 'C'},\n", + " {'variant_id': 'rs429358', 'gene': 'APOE', 'allele': 'C'},\n", + " {'variant_id': 'rs699', 'gene': 'AGT', 'allele': 'G'}],\n", + " 'PMC5634569': [{'variant_id': 'UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'},\n", + " {'variant_id': 'rs4124874', 'gene': 'UGT1A1', 'allele': 'GG'}],\n", + " 'PMC2679107': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'}],\n", + " 'PMC5798660': [{'variant_id': 'HLA-A*24:02',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*24:02'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'},\n", + " {'variant_id': 'rs78239784', 'gene': 'CFHR4', 'allele': 'G'}],\n", + " 'PMC3920089': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28'},\n", + " {'variant_id': 'rs10929302', 'gene': 'UGT1A1', 'allele': 'A'},\n", + " {'variant_id': 'rs2070959', 'gene': 'UGT1A6', 'allele': 'A'},\n", + " {'variant_id': 'rs11692021', 'gene': 'UGT1A9', 'allele': 'C'},\n", + " {'variant_id': 'rs6759892', 'gene': 'UGT1A9', 'allele': 'G'},\n", + " {'variant_id': 'rs1105879', 'gene': 'UGT1A6', 'allele': 'C'}],\n", + " 'PMC3860862': [{'variant_id': 'CYP2C9*1, CYP2C9*2',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'}],\n", + " 'PMC6361127': [{'variant_id': 'UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*60',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*60 + *60/*60'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*60',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*60 + *60/*60'},\n", + " {'variant_id': 'UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*60',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*60 + *60/*60'}],\n", + " 'PMC2536127': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC1472539': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG'}],\n", + " 'PMC3020258': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*1/*3'}],\n", + " 'PMC2919234': [{'variant_id': 'rs7799039', 'gene': 'LEP', 'allele': 'A'}],\n", + " 'PMC3085844': [{'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'A'},\n", + " {'variant_id': 'rs13181', 'gene': 'ERCC2', 'allele': 'G'},\n", + " {'variant_id': 'rs1799793', 'gene': 'ERCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'G'}],\n", + " 'PMC7786995': [{'variant_id': 'rs569356', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs702764', 'gene': 'OPRK1', 'allele': 'C'},\n", + " {'variant_id': 'rs963549', 'gene': 'OPRK1', 'allele': 'T'},\n", + " {'variant_id': 'rs678849', 'gene': 'OPRD1', 'allele': 'T'},\n", + " {'variant_id': 'rs2236857', 'gene': 'OPRD1', 'allele': 'C'},\n", + " {'variant_id': 'rs1042114', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs421300', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs2298896', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs12749204', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs529520', 'gene': 'OPRD1', 'allele': 'C'},\n", + " {'variant_id': 'rs2236855', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs548646', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs997917', 'gene': 'OPRK1', 'allele': 'C'},\n", + " {'variant_id': 'rs12675595', 'gene': 'OPRK1', 'allele': 'A'},\n", + " {'variant_id': 'rs2234918', 'gene': 'OPRD1', 'allele': 'T'},\n", + " {'variant_id': 'rs1051660', 'gene': 'OPRK1', 'allele': 'A'},\n", + " {'variant_id': 'rs6985606', 'gene': 'OPRK1', 'allele': 'T'},\n", + " {'variant_id': 'rs1381376', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs3778156', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs671531', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs2075572', 'gene': 'OPRM1', 'allele': 'C'},\n", + " {'variant_id': 'rs609148', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs524731', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs3823010', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs511435', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs648893', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC3242600': [{'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'TT'}],\n", + " 'PMC3113609': [{'variant_id': 'HLA-A*31:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*31:01'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'}],\n", + " 'PMC5389420': [{'variant_id': 'DPYD low activity',\n", + " 'gene': 'DPYD',\n", + " 'allele': None}],\n", + " 'PMC4839824': [{'variant_id': 'TPMT*1, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*3C'}],\n", + " 'PMC3787223': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*3/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*3/*3'}],\n", + " 'PMC4175895': [{'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3'}],\n", + " 'PMC3894785': [{'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*2 + *1/*3A + *1/*3C'},\n", + " {'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*2 + *1/*3A + *1/*3C'}],\n", + " 'PMC6817966': [{'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'rs987237', 'gene': 'TFAP2B', 'allele': 'G'},\n", + " {'variant_id': 'rs1867785', 'gene': 'EPAS1', 'allele': 'G'}],\n", + " 'PMC2824234': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*1/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*1/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*1/*4'}],\n", + " 'PMC1226035': [{'variant_id': 'rs121918596',\n", + " 'gene': 'RYR1',\n", + " 'allele': 'GAG'}],\n", + " 'PMC3263719': [{'variant_id': 'CYP2D6*4, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*41'},\n", + " {'variant_id': 'CYP2D6*10', 'gene': 'CYP2D6', 'allele': '*10/*10'}],\n", + " 'PMC4696942': [{'variant_id': 'HLA-B*44:03',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*44:03'},\n", + " {'variant_id': 'HLA-B*58:01', 'gene': 'HLA-B', 'allele': '*58:01'}],\n", + " 'PMC2014246': [{'variant_id': 'CYP2D6*1, CYP2D6*5, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*5/*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'}],\n", + " 'PMC2602733': [{'variant_id': 'rs56293913',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs2297595', 'gene': 'DPYD', 'allele': 'CC + CT'}],\n", + " 'PMC4220988': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': None},\n", + " {'variant_id': 'GSTT1 non-null, GSTT1 null',\n", + " 'gene': 'GSTT1',\n", + " 'allele': None},\n", + " {'variant_id': 'rs4880', 'gene': 'SOD2', 'allele': 'AA + AG'}],\n", + " 'PMC2796561': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR long form (L allele)'}],\n", + " 'PMC4992991': [{'variant_id': 'HLA-DQA1*01:02',\n", + " 'gene': 'HLA-DQA1',\n", + " 'allele': '*01:02'},\n", + " {'variant_id': 'HLA-DQB1*02:01', 'gene': 'HLA-DQB1', 'allele': '*02:01'}],\n", + " 'PMC2824479': [{'variant_id': 'rs71103505',\n", + " 'gene': 'BDKRB2',\n", + " 'allele': '(GGTGGGGAC)2/(GGTGGGGAC)2'},\n", + " {'variant_id': 'rs1799752',\n", + " 'gene': 'ACE',\n", + " 'allele': 'ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC'},\n", + " {'variant_id': 'rs1046248', 'gene': 'BDKRB2', 'allele': 'T'}],\n", + " 'PMC4358736': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR long form (L allele)'},\n", + " {'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR long form (L allele)/HTTLPR long form (L allele)'},\n", + " {'variant_id': 'rs57098334', 'gene': None, 'allele': '(CCCACCCGA)12'},\n", + " {'variant_id': 'rs25531', 'gene': 'SLC6A4', 'allele': 'C'}],\n", + " 'PMC4077673': [{'variant_id': 'rs9340799', 'gene': 'ESR1', 'allele': 'GG'}],\n", + " 'PMC4831372': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC6986167': [{'variant_id': 'CYP2C19*1, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*17 + *17/*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*17 + *17/*17'}],\n", + " 'PMC4833150': [{'variant_id': 'rs2279343', 'gene': 'CYP2B6', 'allele': 'G'},\n", + " {'variant_id': 'rs4802101', 'gene': 'CYP2B6', 'allele': 'C'},\n", + " {'variant_id': 'rs4803419', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'A'},\n", + " {'variant_id': 'rs7254579', 'gene': 'CYP2B6', 'allele': 'C'},\n", + " {'variant_id': 'rs13059232', 'gene': 'NR1I2', 'allele': 'C'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'G'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'G'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'G'},\n", + " {'variant_id': 'rs2279343', 'gene': 'CYP2B6', 'allele': 'G'},\n", + " {'variant_id': 'rs13059232', 'gene': 'NR1I2', 'allele': 'T'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs4803419', 'gene': 'CYP2B6', 'allele': 'C'},\n", + " {'variant_id': 'rs4986893', 'gene': 'CYP2C19', 'allele': 'A'},\n", + " {'variant_id': 'rs4802101', 'gene': 'CYP2B6', 'allele': 'C'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'A'}],\n", + " 'PMC11418216': [{'variant_id': 'rs2231137', 'gene': 'ABCG2', 'allele': 'CT'},\n", + " {'variant_id': 'rs182420', 'gene': 'SULT2A1', 'allele': 'CC'}],\n", + " 'PMC6435416': [{'variant_id': 'CYP2D6 poor metabolizer and intermediate metabolizer genotypes',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC2532664': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC1143572': [{'variant_id': 'CYP2D6*1',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4 + *4/*4'},\n", + " {'variant_id': 'rs1042028', 'gene': 'SULT1A1', 'allele': 'CC'}],\n", + " 'PMC2760462': [{'variant_id': 'CYP3A5*1, CYP3A5*3, CYP3A5*6, CYP3A5*7',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*3 + *6 + *7'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3, CYP3A5*6, CYP3A5*7',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*3 + *6 + *7'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'}],\n", + " 'PMC5355161': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'A'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'G'}],\n", + " 'PMC3235041': [{'variant_id': 'CYP2C19*1, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'}],\n", + " 'PMC4579807': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'}],\n", + " 'PMC11677811': [{'variant_id': 'rs1799793',\n", + " 'gene': 'ERCC2',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs4793665', 'gene': 'ABCC3', 'allele': 'TT'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'GG'}],\n", + " 'PMC1262758': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC4304738': [{'variant_id': 'CYP2C9*1, CYP2C9*2',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*2 + *2/*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *2/*3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'TT'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'TT'}],\n", + " 'PMC1841640': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC1629693': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC1840490': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC4514347': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28 + *6/*6 + *1/*6 + *1/*28'},\n", + " {'variant_id': 'rs4148323', 'gene': 'UGT1A1', 'allele': 'AA + AG'}],\n", + " 'PMC4067047': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'CYP4F2*1, CYP4F2*3',\n", + " 'gene': 'CYP4F2',\n", + " 'allele': '*1/*3 + *3/*3'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'CYP4F2*1, CYP4F2*3', 'gene': 'CYP4F2', 'allele': '*3'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'}],\n", + " 'PMC10318569': [{'variant_id': 'rs73823859',\n", + " 'gene': 'UGT2B7',\n", + " 'allele': 'A'}],\n", + " 'PMC10339303': [{'variant_id': 'rs671',\n", + " 'gene': 'ALDH2',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC3879795': [{'variant_id': 'CYP2C9*1, CYP2C9*8',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*8'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*8', 'gene': 'CYP2C9', 'allele': '*8'}],\n", + " 'PMC3611934': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4'},\n", + " {'variant_id': 'UGT2B7', 'gene': 'UGT2B7', 'allele': '*2/*2'},\n", + " {'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC2673121': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'}],\n", + " 'PMC5098919': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *2/*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*3 + *3/*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *3/*3'},\n", + " {'variant_id': 'rs2242480', 'gene': 'CYP3A4', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1934980', 'gene': 'CYP2C8', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs17110453', 'gene': 'CYP2C8', 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'CC + CT'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *2/*2'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'CC + CT'}],\n", + " 'PMC7501182': [{'variant_id': 'UGT2B17 deficiency',\n", + " 'gene': 'UGT2B17',\n", + " 'allele': None},\n", + " {'variant_id': 'UGT2B17 deficiency', 'gene': 'UGT2B17', 'allele': None},\n", + " {'variant_id': 'UGT2B17 deficiency', 'gene': 'UGT2B17', 'allele': None}],\n", + " 'PMC10327396': [{'variant_id': 'rs4728709', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs4148738', 'gene': 'ABCB1', 'allele': 'CC'},\n", + " {'variant_id': 'rs4728709', 'gene': 'ABCB1', 'allele': 'GG'}],\n", + " 'PMC6245894': [{'variant_id': 'rs880395', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs1948', 'gene': 'CHRNB4', 'allele': 'A'},\n", + " {'variant_id': 'rs4887074', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs16969968', 'gene': 'CHRNA3, CHRNA5', 'allele': 'A'}],\n", + " 'PMC4168188': [{'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'AA'}],\n", + " 'PMC3291182': [{'variant_id': 'CYP2D6*1, CYP2D6*4, CYP2D6*5, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10 + *4/*10 + *5/*10'}],\n", + " 'PMC5306492': [{'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs730154', 'gene': 'CYP19A1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs11849538', 'gene': 'TCL1A', 'allele': 'CG + GG'},\n", + " {'variant_id': 'rs749292', 'gene': 'CYP19A1', 'allele': 'AA'},\n", + " {'variant_id': 'rs727479', 'gene': 'CYP19A1', 'allele': 'CC'},\n", + " {'variant_id': 'rs749292', 'gene': 'CYP19A1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1008805', 'gene': 'CYP19A1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs727479', 'gene': 'CYP19A1', 'allele': 'AA'},\n", + " {'variant_id': 'rs7159713', 'gene': 'TCL1A', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1008805', 'gene': 'CYP19A1', 'allele': 'GG'},\n", + " {'variant_id': 'rs730154', 'gene': 'CYP19A1', 'allele': 'TT'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'AA'}],\n", + " 'PMC4917605': [{'variant_id': 'NAT2*4', 'gene': 'NAT2', 'allele': '*4'},\n", + " {'variant_id': 'NAT2*4, NAT2*6', 'gene': 'NAT2', 'allele': '*6'}],\n", + " 'PMC5710192': [{'variant_id': 'rs143414470', 'gene': 'ANK3', 'allele': 'T'},\n", + " {'variant_id': 'rs144470777', 'gene': 'NSUN3', 'allele': 'C'},\n", + " {'variant_id': 'rs187805828', 'gene': 'PHC1', 'allele': 'C'},\n", + " {'variant_id': 'rs116134453', 'gene': 'FAT1', 'allele': 'T'},\n", + " {'variant_id': 'rs141531882', 'gene': 'TENM4', 'allele': 'C'},\n", + " {'variant_id': 'rs77475703', 'gene': 'CD96', 'allele': 'C'},\n", + " {'variant_id': 'rs117484357', 'gene': 'ZMIZ1', 'allele': 'C'},\n", + " {'variant_id': 'rs138385713', 'gene': 'VPS13D', 'allele': 'G'},\n", + " {'variant_id': 'rs141213385', 'gene': 'CCDC77', 'allele': 'G'},\n", + " {'variant_id': 'rs117308378', 'gene': 'ADGRG7', 'allele': 'T'},\n", + " {'variant_id': 'rs75267292', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs146898897', 'gene': 'SSU72', 'allele': 'C'},\n", + " {'variant_id': 'rs185346775', 'gene': 'MAN1A1', 'allele': 'A'},\n", + " {'variant_id': 'rs72765700', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs10024471', 'gene': 'MIR2054', 'allele': 'A'},\n", + " {'variant_id': 'rs117876855', 'gene': 'SPRY2', 'allele': 'A'},\n", + " {'variant_id': 'rs139544515', 'gene': 'SPRY2', 'allele': 'A'},\n", + " {'variant_id': 'rs185217050', 'gene': 'NCOA7', 'allele': 'T'},\n", + " {'variant_id': 'rs139368788', 'gene': 'CCDC70', 'allele': 'A'},\n", + " {'variant_id': 'rs117412990', 'gene': 'TMEM131L', 'allele': 'G'},\n", + " {'variant_id': 'rs191934521', 'gene': 'SIRPA', 'allele': 'G'},\n", + " {'variant_id': 'rs146644707', 'gene': 'LMNTD1', 'allele': 'T'}],\n", + " 'PMC3596857': [{'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'CC'},\n", + " {'variant_id': 'rs28399499', 'gene': 'CYP2B6', 'allele': 'CT'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs28399499', 'gene': 'CYP2B6', 'allele': 'C'}],\n", + " 'PMC3263869': [{'variant_id': 'rs6265', 'gene': 'BDNF', 'allele': 'C'},\n", + " {'variant_id': 'rs2815752', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs1801282', 'gene': 'PPARG', 'allele': 'G'},\n", + " {'variant_id': 'rs1801282', 'gene': 'PPARG', 'allele': 'G'}],\n", + " 'PMC3260968': [{'variant_id': 'rs5751876',\n", + " 'gene': 'ADORA2A',\n", + " 'allele': 'TT'}],\n", + " 'PMC3686845': [{'variant_id': 'rs35599367',\n", + " 'gene': 'CYP3A4',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC3160494': [{'variant_id': 'rs1649942', 'gene': 'NRG3', 'allele': 'G'}],\n", + " 'PMC3192752': [{'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'C'}],\n", + " 'PMC5823653': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1801516', 'gene': 'ATM', 'allele': 'AA'},\n", + " {'variant_id': 'rs1056836', 'gene': 'CYP1B1', 'allele': 'CC'},\n", + " {'variant_id': 'rs1801516', 'gene': 'ATM', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'AA'},\n", + " {'variant_id': 'rs20572', 'gene': 'CBR1', 'allele': 'TT'},\n", + " {'variant_id': 'rs1042522', 'gene': 'TP53', 'allele': 'GG'},\n", + " {'variant_id': 'rs714368', 'gene': 'SLC22A16', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs25487', 'gene': 'XRCC1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs12248560', 'gene': 'CYP2C19', 'allele': 'CC'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GT'},\n", + " {'variant_id': 'rs2228100', 'gene': 'ALDH3A1', 'allele': 'CG + GG'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'GG'},\n", + " {'variant_id': 'rs6907567', 'gene': 'SLC22A16', 'allele': 'AA'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1801159', 'gene': 'DPYD', 'allele': 'TT'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'AG + GG'}],\n", + " 'PMC5711795': [{'variant_id': 'rs67624739',\n", + " 'gene': 'CHRNA5',\n", + " 'allele': 'del'},\n", + " {'variant_id': 'rs55853698', 'gene': 'CHRNA5', 'allele': 'G'},\n", + " {'variant_id': 'rs55781567', 'gene': 'CHRNA5', 'allele': 'G'},\n", + " {'variant_id': 'rs503464', 'gene': 'CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs2236196', 'gene': 'CHRNA4', 'allele': 'G'},\n", + " {'variant_id': 'rs16969968', 'gene': 'CHRNA3, CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs2072661', 'gene': 'CHRNB2', 'allele': 'A'},\n", + " {'variant_id': 'rs67624739', 'gene': 'CHRNA5', 'allele': 'del'},\n", + " {'variant_id': 'rs55781567', 'gene': 'CHRNA5', 'allele': 'G'},\n", + " {'variant_id': 'rs16969968', 'gene': 'CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs2236196', 'gene': 'CHRNA4', 'allele': 'G'},\n", + " {'variant_id': 'rs503464', 'gene': 'CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs55853698', 'gene': 'CHRNA5', 'allele': 'G'},\n", + " {'variant_id': 'rs2072661', 'gene': 'CHRNB2', 'allele': 'A'}],\n", + " 'PMC3055635': [{'variant_id': 'rs5751876', 'gene': 'ADORA2A', 'allele': 'TT'},\n", + " {'variant_id': 'rs2267076', 'gene': 'ADORA2A', 'allele': 'TT'},\n", + " {'variant_id': 'rs3761422', 'gene': 'ADORA2A', 'allele': 'TT'},\n", + " {'variant_id': 'rs11704959', 'gene': None, 'allele': 'AC'},\n", + " {'variant_id': 'rs2298383', 'gene': 'ADORA2A', 'allele': 'CC'},\n", + " {'variant_id': 'rs5760405', 'gene': 'SPECC1L', 'allele': 'TT'},\n", + " {'variant_id': 'rs5751862', 'gene': 'SPECC1L', 'allele': 'AA'}],\n", + " 'PMC4599525': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'}],\n", + " 'PMC3548984': [{'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*3 + *4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*6',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*3 + *1/*4 + *1/*6'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*10, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*10 + *1/*41 + *10/*10 + *41/*41'}],\n", + " 'PMC2699901': [{'variant_id': 'rs1799978', 'gene': 'DRD2', 'allele': 'C'},\n", + " {'variant_id': 'rs1800497', 'gene': 'ANKK1', 'allele': 'AA + AG'}],\n", + " 'PMC3681433': [{'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*5, CYP2D6*10, CYP2D6*14',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*1 + *1/*2 + *2/*2'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'}],\n", + " 'PMC2884029': [{'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'A'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'A'},\n", + " {'variant_id': 'rs4149117', 'gene': 'SLCO1B3', 'allele': 'G'},\n", + " {'variant_id': 'rs4149117', 'gene': 'SLCO1B3', 'allele': 'G'}],\n", + " 'PMC4209173': [{'variant_id': 'CYP2C9*1, CYP2C9*2',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'CT'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'}],\n", + " 'PMC2000724': [{'variant_id': 'CYP2D6*1, CYP2D6*4, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10 + *4'}],\n", + " 'PMC3788742': [{'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*35, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4 + *5 + *10 + *41'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*35, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4 + *5 + *10 + *41'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*35, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4 + *5 + *10 + *41'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*35, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4 + *5 + *10 + *41'}],\n", + " 'PMC2603295': [{'variant_id': 'rs1050828',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1050828', 'gene': 'G6PD', 'allele': 'CT + TT'}],\n", + " 'PMC2714371': [{'variant_id': 'rs6603859', 'gene': 'SZRD1', 'allele': 'T'},\n", + " {'variant_id': 'rs7929521', 'gene': None, 'allele': 'GG'},\n", + " {'variant_id': 'rs3750518', 'gene': 'MAN1B1', 'allele': 'T'},\n", + " {'variant_id': 'rs10932125', 'gene': 'NRP2', 'allele': 'C'},\n", + " {'variant_id': 'rs10932125', 'gene': 'NRP2', 'allele': 'C'}],\n", + " 'PMC3610662': [{'variant_id': 'CYP2D6*1, CYP2D6*1xN, CYP2D6*2, CYP2D6*2xN, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*7',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*4 + *4/*4 + *4/*5 + *4/*6 + *4/*7'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*7',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*4 + *4/*4 + *4/*5 + *4/*6 + *4/*7'}],\n", + " 'PMC3249195': [{'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*4, CYP2D6*6',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4 + *4/*6'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*3, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4 + *3/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*4, CYP2D6*6',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4 + *4/*6'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*3, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4 + *3/*4'}],\n", + " 'PMC6571425': [{'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'T'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC2572782': [{'variant_id': 'NAT1*4, NAT1*11B',\n", + " 'gene': 'NAT1',\n", + " 'allele': '*11B/*4'},\n", + " {'variant_id': 'NAT1*10, NAT1*11A', 'gene': 'NAT1', 'allele': '*10/*11A'},\n", + " {'variant_id': 'NAT1*4, NAT1*10', 'gene': 'NAT1', 'allele': '*10/*4'},\n", + " {'variant_id': 'NAT1*3, NAT1*11B', 'gene': 'NAT1', 'allele': '*11B/*3'},\n", + " {'variant_id': 'NAT1*10', 'gene': 'NAT1', 'allele': '*10/*10'},\n", + " {'variant_id': 'NAT1*10, NAT1*11B', 'gene': 'NAT1', 'allele': '*10/*11B'},\n", + " {'variant_id': 'NAT1*4, NAT1*11A', 'gene': 'NAT1', 'allele': '*11A/*4'},\n", + " {'variant_id': 'rs2066853', 'gene': 'AHR', 'allele': 'AA + AG'}],\n", + " 'PMC5736261': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC3760990': [{'variant_id': 'CYP2D6*1, CYP2D6*2',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*2 + *2/*2'},\n", + " {'variant_id': 'CYP2C8*1, CYP2C8*2',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': '*1/*2 + *2/*2'}],\n", + " 'PMC6403100': [{'variant_id': 'DPYD deficiency',\n", + " 'gene': 'DPYD',\n", + " 'allele': None}],\n", + " 'PMC3307155': [{'variant_id': 'rs8099917', 'gene': 'IFNL3', 'allele': 'TT'},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'CC'},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'CC'},\n", + " {'variant_id': 'rs8099917', 'gene': 'IFNL3', 'allele': 'TT'}],\n", + " 'PMC4704657': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*3'}],\n", + " 'PMC5207541': [{'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'CT + TT'}],\n", + " 'PMC5467955': [{'variant_id': 'HLA-DRB1*08:02',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*08:02'},\n", + " {'variant_id': 'HLA-DRB1*12:02', 'gene': 'HLA-DRB1', 'allele': '*12:02'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*54:01', 'gene': 'HLA-B', 'allele': '*54:01'},\n", + " {'variant_id': 'HLA-B*46:01', 'gene': 'HLA-B', 'allele': '*46:01'},\n", + " {'variant_id': 'HLA-B*51:02', 'gene': 'HLA-B', 'allele': '*51:02'},\n", + " {'variant_id': 'HLA-C*01:02', 'gene': 'HLA-C', 'allele': '*01:02'},\n", + " {'variant_id': 'HLA-C*03:04', 'gene': 'HLA-C', 'allele': '*03:04'},\n", + " {'variant_id': 'HLA-DRB1*08:03', 'gene': 'HLA-DRB1', 'allele': '*08:03'},\n", + " {'variant_id': 'HLA-C*15:05', 'gene': 'HLA-C', 'allele': '*15:05'},\n", + " {'variant_id': 'HLA-B*13:01', 'gene': 'HLA-B', 'allele': '*13:01'},\n", + " {'variant_id': 'HLA-A*29:01', 'gene': 'HLA-A', 'allele': '*29:01'},\n", + " {'variant_id': 'HLA-B*07:05', 'gene': 'HLA-B', 'allele': '*07:05'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*13:25', 'gene': 'HLA-B', 'allele': '*13:25'},\n", + " {'variant_id': 'HLA-B*15:01', 'gene': 'HLA-B', 'allele': '*15:01'},\n", + " {'variant_id': 'HLA-B*38:02', 'gene': 'HLA-B', 'allele': '*38:02'},\n", + " {'variant_id': 'HLA-B*40:02', 'gene': 'HLA-B', 'allele': '*40:02'},\n", + " {'variant_id': 'HLA-DRB1*11:01', 'gene': 'HLA-DRB1', 'allele': '*11:01'},\n", + " {'variant_id': 'HLA-DRB1*09:01', 'gene': 'HLA-DRB1', 'allele': '*09:01'},\n", + " {'variant_id': 'HLA-DRB1*15:01', 'gene': 'HLA-DRB1', 'allele': '*15:01'},\n", + " {'variant_id': 'HLA-DRB1*15:02', 'gene': 'HLA-DRB1', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-DRB1*14:54', 'gene': 'HLA-DRB1', 'allele': '*14:54'},\n", + " {'variant_id': 'HLA-DRB1*16:02', 'gene': 'HLA-DRB1', 'allele': '*16:02'},\n", + " {'variant_id': 'HLA-A*02:01', 'gene': 'HLA-A', 'allele': '*02:01'},\n", + " {'variant_id': 'HLA-A*24:02', 'gene': 'HLA-A', 'allele': '*24:02'},\n", + " {'variant_id': 'HLA-A*02:07', 'gene': 'HLA-A', 'allele': '*02:07'},\n", + " {'variant_id': 'HLA-A*11:01', 'gene': 'HLA-A', 'allele': '*11:01'},\n", + " {'variant_id': 'HLA-A*02:03', 'gene': 'HLA-A', 'allele': '*02:03'},\n", + " {'variant_id': 'HLA-A*02:06', 'gene': 'HLA-A', 'allele': '*02:06'},\n", + " {'variant_id': 'HLA-B*55:02', 'gene': 'HLA-B', 'allele': '*55:02'},\n", + " {'variant_id': 'HLA-B*56:01', 'gene': 'HLA-B', 'allele': '*56:01'},\n", + " {'variant_id': 'HLA-C*03:03', 'gene': 'HLA-C', 'allele': '*03:03'},\n", + " {'variant_id': 'HLA-C*04:01', 'gene': 'HLA-C', 'allele': '*04:01'},\n", + " {'variant_id': 'HLA-C*07:02', 'gene': 'HLA-C', 'allele': '*07:02'},\n", + " {'variant_id': 'HLA-C*08:01', 'gene': 'HLA-C', 'allele': '*08:01'},\n", + " {'variant_id': 'HLA-B*35:01', 'gene': 'HLA-B', 'allele': '*35:01'},\n", + " {'variant_id': 'HLA-B*40:01', 'gene': 'HLA-B', 'allele': '*40:01'},\n", + " {'variant_id': 'HLA-DRB1*12:01', 'gene': 'HLA-DRB1', 'allele': '*12:01'}],\n", + " 'PMC3607698': [{'variant_id': 'rs1864729', 'gene': None, 'allele': 'A'}],\n", + " 'PMC4506516': [{'variant_id': 'HLA-DRB1*08:03',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*08:03'},\n", + " {'variant_id': 'HLA-B*38:02', 'gene': 'HLA-B', 'allele': '*38:02'}],\n", + " 'PMC6249237': [{'variant_id': 'DPYD low activity',\n", + " 'gene': 'DPYD',\n", + " 'allele': None},\n", + " {'variant_id': 'UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28/*28'}],\n", + " 'PMC2012941': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC7423633': [{'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': None}],\n", + " 'PMC3411889': [{'variant_id': 'rs10033464', 'gene': None, 'allele': 'GG'},\n", + " {'variant_id': 'rs10033464', 'gene': None, 'allele': 'GT + TT'}],\n", + " 'PMC2268970': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3 +*17'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'C'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 +*4 + *5 + *6'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'G'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'G'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'T'}],\n", + " 'PMC6041857': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)3'}],\n", + " 'PMC4937952': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3'}],\n", + " 'PMC4930967': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*6 + *28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *6/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*6/*6 + *1/*6 + *6/*28'}],\n", + " 'PMC5600645': [{'variant_id': 'HLA-DRB3*01:01',\n", + " 'gene': 'HLA-DRB3',\n", + " 'allele': '*01:01'}],\n", + " 'PMC4570031': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'}],\n", + " 'PMC5331771': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'},\n", + " {'variant_id': 'rs2046934', 'gene': 'P2RY12', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs2046934', 'gene': 'P2RY12', 'allele': 'AG + GG'}],\n", + " 'PMC3349071': [{'variant_id': 'CYP2A6*1, CYP2A6*4, CYP2A6*9, CYP2A6*10',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*4 + *10 + *9'}],\n", + " 'PMC4964997': [{'variant_id': 'rs121918596', 'gene': 'RYR1', 'allele': 'del'},\n", + " {'variant_id': 'rs727504129', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs121918596', 'gene': 'RYR1', 'allele': 'del'},\n", + " {'variant_id': 'rs727504129', 'gene': 'RYR1', 'allele': 'T'}],\n", + " 'PMC4478047': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'C'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'}],\n", + " 'PMC3154335': [{'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*5, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*5/*10 + *10/*10 + *1/*10 + *1/*5'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*5, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*5/*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*5, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*5/*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*5, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*5/*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*5, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*5/*10'}],\n", + " 'PMC5806861': [{'variant_id': 'rs961253', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs20417', 'gene': 'PTGS2', 'allele': 'C'},\n", + " {'variant_id': 'rs5275', 'gene': 'PTGS2', 'allele': 'A'},\n", + " {'variant_id': 'rs5277', 'gene': 'PTGS2', 'allele': 'G'},\n", + " {'variant_id': 'rs11694911', 'gene': 'ODC1', 'allele': 'C'},\n", + " {'variant_id': 'rs4648310', 'gene': 'PTGS2', 'allele': 'T'},\n", + " {'variant_id': 'rs2745557', 'gene': 'PTGS2', 'allele': 'A'},\n", + " {'variant_id': 'rs2302615', 'gene': 'ODC1', 'allele': 'C'},\n", + " {'variant_id': 'rs2430420', 'gene': 'ODC1', 'allele': 'A'},\n", + " {'variant_id': 'rs4936367', 'gene': 'PAFAH1B2', 'allele': 'A'},\n", + " {'variant_id': 'rs7112513', 'gene': 'PAFAH1B2', 'allele': 'A'},\n", + " {'variant_id': 'rs3842787', 'gene': 'PTGS1', 'allele': 'C'},\n", + " {'variant_id': 'rs2619112', 'gene': 'ALOX15', 'allele': 'C'},\n", + " {'variant_id': 'rs230490', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs11986055', 'gene': 'IKBKB', 'allele': 'A'},\n", + " {'variant_id': 'rs12910333', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs10958713', 'gene': 'IKBKB', 'allele': 'C'},\n", + " {'variant_id': 'rs5029748', 'gene': 'IKBKB', 'allele': 'G'},\n", + " {'variant_id': 'rs5995355', 'gene': 'NCF4', 'allele': 'A'},\n", + " {'variant_id': 'rs6474387', 'gene': 'IKBKB', 'allele': 'C'},\n", + " {'variant_id': 'rs6983267', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs28362380', 'gene': 'ODC1', 'allele': 'C'},\n", + " {'variant_id': 'rs1105879', 'gene': 'UGT1A6', 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs2070959', 'gene': 'UGT1A6', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs1799853', 'gene': 'CYP2C9', 'allele': 'C'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'A'},\n", + " {'variant_id': 'rs7349744', 'gene': 'HPGD', 'allele': 'A'},\n", + " {'variant_id': 'rs17239025', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs2965667', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs6683455', 'gene': 'IL23R', 'allele': 'T'}],\n", + " 'PMC3536666': [{'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*10, CYP2D6*114',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*10, CYP2D6*114',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'}],\n", + " 'PMC4137829': [{'variant_id': 'rs1065852', 'gene': 'CYP2D6', 'allele': 'A'},\n", + " {'variant_id': 'rs1074145', 'gene': None, 'allele': 'A'}],\n", + " 'PMC2903324': [{'variant_id': 'CYP3A4*1, CYP3A4*3',\n", + " 'gene': 'CYP3A4',\n", + " 'allele': '*1 + *3'},\n", + " {'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': 'null/null'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28/*28'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GG'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'GG'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'GG'},\n", + " {'variant_id': 'rs25487', 'gene': 'XRCC1', 'allele': 'CC'},\n", + " {'variant_id': 'rs10929302', 'gene': 'UGT1A1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1801265', 'gene': 'DPYD', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs1801159', 'gene': 'DPYD', 'allele': 'CC + CT'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28/*28'},\n", + " {'variant_id': 'CYP3A4*1, CYP3A4*3', 'gene': 'CYP3A4', 'allele': '*1 + *3'},\n", + " {'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': 'null/null'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs1801159', 'gene': 'DPYD', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs1801265', 'gene': 'DPYD', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs25487', 'gene': 'XRCC1', 'allele': 'CC'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GG'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'GG'},\n", + " {'variant_id': 'rs10929302', 'gene': 'UGT1A1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'AA'}],\n", + " 'PMC3660078': [{'variant_id': 'rs10509681', 'gene': 'CYP2C8', 'allele': 'C'}],\n", + " 'PMC7444994': [{'variant_id': 'HLA-B*57:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*57:01'}],\n", + " 'PMC3090085': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*4', 'gene': 'CYP2C19', 'allele': '*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17', 'gene': 'CYP2C19', 'allele': '*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19 poor metabolizers',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'A'}],\n", + " 'PMC4017868': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR short form (S allele)/HTTLPR short form (S allele)'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1799732', 'gene': 'DRD2', 'allele': 'GG'}],\n", + " 'PMC2480976': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'}],\n", + " 'PMC2630264': [{'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'rs3814637', 'gene': 'CYP2C19', 'allele': 'C'},\n", + " {'variant_id': 'rs4917639', 'gene': 'CYP2C9', 'allele': 'A'},\n", + " {'variant_id': 'rs2359612', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3/*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2/*2 + *2/*3 + *3/*3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'TT'}],\n", + " 'PMC5414099': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2'}],\n", + " 'PMC3207003': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*41', 'gene': 'CYP2D6', 'allele': '*41'},\n", + " {'variant_id': 'CYP2D6*17', 'gene': 'CYP2D6', 'allele': '*17'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*10', 'gene': 'CYP2D6', 'allele': '*10'},\n", + " {'variant_id': 'CYP2D6*3', 'gene': 'CYP2D6', 'allele': '*3'},\n", + " {'variant_id': 'CYP2D6*1', 'gene': 'CYP2D6', 'allele': '*1'},\n", + " {'variant_id': 'CYP2D6*6', 'gene': 'CYP2D6', 'allele': '*6'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*5', 'gene': 'CYP2D6', 'allele': '*5'}],\n", + " 'PMC4226857': [{'variant_id': 'HLA-B*40:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*40:01'},\n", + " {'variant_id': 'HLA-B*35:05', 'gene': 'HLA-B', 'allele': '*35:05'}],\n", + " 'PMC6439872': [{'variant_id': 'rs746071566',\n", + " 'gene': 'NUDT15',\n", + " 'allele': 'del'},\n", + " {'variant_id': 'rs11969064', 'gene': None, 'allele': 'T'}],\n", + " 'PMC554812': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'},\n", + " {'variant_id': 'HLA-DRB1*03:01', 'gene': 'HLA-DRB1', 'allele': '*03:01'},\n", + " {'variant_id': 'HLA-C*03:02', 'gene': 'HLA-C', 'allele': '*03:02'},\n", + " {'variant_id': 'HLA-A*33:03', 'gene': 'HLA-A', 'allele': '*33:03'},\n", + " {'variant_id': 'rs1594', 'gene': 'CFLAR', 'allele': 'A'}],\n", + " 'PMC3859235': [{'variant_id': 'rs35068180',\n", + " 'gene': 'MMP3',\n", + " 'allele': 'del/del'},\n", + " {'variant_id': 'rs35068180', 'gene': 'MMP3', 'allele': 'AA'}],\n", + " 'PMC1617109': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC2265313': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR short form (S allele)'}],\n", + " 'PMC2432488': [{'variant_id': 'SLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR short form (S allele)'}],\n", + " 'PMC6767867': [{'variant_id': 'rs11322783',\n", + " 'gene': 'IFNL4',\n", + " 'allele': 'TT/TT'},\n", + " {'variant_id': 'rs11322783', 'gene': 'IFNL4', 'allele': 'TT/TT'}],\n", + " 'PMC5533497': [{'variant_id': 'CYP2D6 poor and ultrarapid metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor and ultrarapid metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC3394799': [{'variant_id': 'CYP2B6*1, CYP2B6*6',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*6'}],\n", + " 'PMC3776990': [{'variant_id': 'UGT1A1*60', 'gene': 'UGT1A1', 'allele': '*60'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*4, CYP2A6*7, CYP2A6*9',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*4 + *7 + *9'},\n", + " {'variant_id': 'UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6'},\n", + " {'variant_id': 'UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A6*2a', 'gene': 'UGT1A6', 'allele': '*2a'},\n", + " {'variant_id': 'UGT1A7*3', 'gene': 'UGT1A7', 'allele': '*3'},\n", + " {'variant_id': 'UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6'},\n", + " {'variant_id': 'CYP2A6*1, CYP2A6*4, CYP2A6*7, CYP2A6*9',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*4 + *7 + *9'},\n", + " {'variant_id': 'UGT1A7*3', 'gene': 'UGT1A7', 'allele': '*3'},\n", + " {'variant_id': 'UGT1A1*60', 'gene': 'UGT1A1', 'allele': '*60'},\n", + " {'variant_id': 'UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A7*3', 'gene': 'UGT1A7', 'allele': '*3'},\n", + " {'variant_id': 'CYP2A6*4, CYP2A6*7, CYP2A6*9',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*4 + *7 + *9'},\n", + " {'variant_id': 'UGT1A6*2a', 'gene': 'UGT1A6', 'allele': '*2a'},\n", + " {'variant_id': 'UGT1A6*2a', 'gene': 'UGT1A6', 'allele': '*2a'},\n", + " {'variant_id': 'UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6'},\n", + " {'variant_id': 'UGT1A1*60', 'gene': 'UGT1A1', 'allele': '*60'},\n", + " {'variant_id': 'UGT1A7*3', 'gene': 'UGT1A7', 'allele': '*3'},\n", + " {'variant_id': 'UGT1A6*2a', 'gene': 'UGT1A6', 'allele': '*2a'}],\n", + " 'PMC5065384': [{'variant_id': 'CYP2B6*1, CYP2B6*6',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*6/*6'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*6', 'gene': 'CYP2B6', 'allele': '*6/*6'}],\n", + " 'PMC2789741': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'}],\n", + " 'PMC4916189': [{'variant_id': 'CYP2B6*1, CYP2B6*9',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*9'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs2472677', 'gene': 'NR1I2', 'allele': 'TT'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs28399499', 'gene': 'CYP2B6', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs4803419', 'gene': 'CYP2B6', 'allele': 'CT + TT'}],\n", + " 'PMC3805522': [{'variant_id': 'rs11479', 'gene': 'TYMP', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs75017182', 'gene': 'DPYD', 'allele': 'CG'},\n", + " {'variant_id': 'rs56038477', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs56038477', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs2236225', 'gene': 'MTHFD1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'AA'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'GG'},\n", + " {'variant_id': 'rs1801019', 'gene': 'UMPS', 'allele': 'CC'},\n", + " {'variant_id': 'rs2072671', 'gene': 'CDA', 'allele': 'CC'},\n", + " {'variant_id': 'rs112723255', 'gene': 'TYMP', 'allele': 'TT'},\n", + " {'variant_id': 'rs11479', 'gene': 'TYMP', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1979277', 'gene': 'SHMT1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs75017182', 'gene': 'DPYD', 'allele': 'CG'}],\n", + " 'PMC7368113': [{'variant_id': 'rs11280056',\n", + " 'gene': 'TYMS',\n", + " 'allele': 'TTA/TTA'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'}],\n", + " 'PMC7230684': [{'variant_id': 'rs11280056',\n", + " 'gene': 'TYMS',\n", + " 'allele': 'TTA/TTA'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'TT'}],\n", + " 'PMC4744681': [{'variant_id': 'CYP2B6*1, CYP2B6*9',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*9/*9 + *1/*9'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'TT'}],\n", + " 'PMC4168388': [{'variant_id': 'CYP2D6*4, CYP2D6*5',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4 + *4/*5'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*1xN',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*1xN + *1xN/*1xN'},\n", + " {'variant_id': 'CYP3A7*1A, CYP3A7*1C',\n", + " 'gene': 'CYP3A7',\n", + " 'allele': '*1C/*1C'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'TT'},\n", + " {'variant_id': 'rs1057868', 'gene': 'POR', 'allele': 'TT'},\n", + " {'variant_id': 'rs1523130', 'gene': 'NR1I2', 'allele': 'CC'},\n", + " {'variant_id': 'rs2472677', 'gene': 'NR1I2', 'allele': 'TT'},\n", + " {'variant_id': 'rs7643645', 'gene': 'NR1I2', 'allele': 'AA'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs4646437', 'gene': 'CYP3A4', 'allele': 'GG'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'TT'},\n", + " {'variant_id': 'rs35599367', 'gene': 'CYP3A4', 'allele': 'GG'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'CC'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'CYP2D6*4, CYP2D6*5',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4 + *4/*5'}],\n", + " 'PMC2364770': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28'}],\n", + " 'PMC3376437': [{'variant_id': 'rs11045585', 'gene': 'SLCO1B3', 'allele': 'G'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'A'},\n", + " {'variant_id': 'rs4149118', 'gene': 'SLCO1B3', 'allele': 'G'},\n", + " {'variant_id': 'rs3834935', 'gene': 'SLCO1B3', 'allele': 'A'},\n", + " {'variant_id': 'rs4149118', 'gene': 'SLCO1B3', 'allele': 'G'},\n", + " {'variant_id': 'rs11045585', 'gene': 'SLCO1B3', 'allele': 'G'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'A'}],\n", + " 'PMC4196861': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'}],\n", + " 'PMC3125053': [{'variant_id': 'rs2279020', 'gene': 'GABRA1', 'allele': 'GG'},\n", + " {'variant_id': 'rs2279020', 'gene': 'GABRA1', 'allele': 'G'},\n", + " {'variant_id': 'rs211037', 'gene': 'GABRG2', 'allele': 'T'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'T'},\n", + " {'variant_id': 'rs2298771', 'gene': 'SCN1A', 'allele': 'C'},\n", + " {'variant_id': 'rs17183814', 'gene': 'SCN2A', 'allele': 'A'}],\n", + " 'PMC4256153': [{'variant_id': 'CYP2C8*2, CYP2C8*3, CYP2C8*4',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': '*2 + *3 + *4'},\n", + " {'variant_id': 'rs492338', 'gene': 'ABCG1', 'allele': 'AA'},\n", + " {'variant_id': 'rs492338', 'gene': 'ABCG1', 'allele': 'AA'}],\n", + " 'PMC3706603': [{'variant_id': 'HLA-A*30:02',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*30:02'},\n", + " {'variant_id': 'HLA-B*18:01', 'gene': 'HLA-B', 'allele': '*18:01'},\n", + " {'variant_id': 'HLA-DRB1*15:01', 'gene': 'HLA-DRB1', 'allele': '*15:01'},\n", + " {'variant_id': 'HLA-DQB1*06:02', 'gene': 'HLA-DQB1', 'allele': '*06:02'}],\n", + " 'PMC7264743': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC2757655': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3, CYP2C9*5, CYP2C9*6, CYP2C9*11',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3 + *5 + *6 + *11'},\n", + " {'variant_id': 'rs7294', 'gene': 'VKORC1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs8050894', 'gene': 'VKORC1', 'allele': 'CG + GG'},\n", + " {'variant_id': 'rs2359612', 'gene': 'VKORC1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3, CYP2C9*5, CYP2C9*6, CYP2C9*11',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3 + *5 + *6 + *11'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3, CYP2C9*5, CYP2C9*6, CYP2C9*11',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3 + *5 + *6 + *11'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3, CYP2C9*5, CYP2C9*6, CYP2C9*11',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3 + *5 + *6 + *11'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'AA + AG'}],\n", + " 'PMC4111800': [{'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*10, CYP2D6*17, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*17 + *3/*4 + *4/*10 + *4/*17 + *4/*4 + *4/*41 + *4/*5 + *4/*6 + *5/*10 + *5/*17'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*1xN, CYP2D6*2, CYP2D6*2xN, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*10, CYP2D6*17, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*17 + *3/*4 + *4/*10 + *4/*17 + *4/*4 + *4/*41 + *4/*5 + *4/*6 + *5/*10 + *5/*17'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*1xN, CYP2D6*2, CYP2D6*2xN, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*10, CYP2D6*17, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*17 + *3/*4 + *4/*10 + *4/*17 + *4/*4 + *4/*41 + *4/*5 + *4/*6 + *5/*10 + *5/*17'}],\n", + " 'PMC4038630': [{'variant_id': 'HLA-DRB1*08:02',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*08:02'},\n", + " {'variant_id': 'HLA-DQB1*04:02', 'gene': 'HLA-DQB1', 'allele': '*04:02'}],\n", + " 'PMC5628515': [{'variant_id': 'TPMT deficiency',\n", + " 'gene': 'TPMT',\n", + " 'allele': None},\n", + " {'variant_id': 'ABCC4 deficiency', 'gene': 'ABCC4', 'allele': None},\n", + " {'variant_id': 'ABCC4 deficiency', 'gene': 'ABCC4', 'allele': None},\n", + " {'variant_id': 'TPMT deficiency', 'gene': 'TPMT', 'allele': None}],\n", + " 'PMC42614': [{'variant_id': 'TPMT*2', 'gene': 'TPMT', 'allele': '*2'},\n", + " {'variant_id': 'TPMT*1, TPMT*2', 'gene': 'TPMT', 'allele': '*2'}],\n", + " 'PMC4772424': [{'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'CT'},\n", + " {'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'CT'}],\n", + " 'PMC3412624': [{'variant_id': 'rs1808458', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs11644322', 'gene': 'WWOX', 'allele': 'T'},\n", + " {'variant_id': 'rs11062040', 'gene': 'DCP1B', 'allele': 'T'},\n", + " {'variant_id': 'rs2900174', 'gene': 'PRB2', 'allele': 'G'},\n", + " {'variant_id': 'rs7771466', 'gene': 'IL17F', 'allele': 'AC'},\n", + " {'variant_id': 'rs763780', 'gene': 'IL17F', 'allele': 'CT'},\n", + " {'variant_id': 'rs179619', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs10736526', 'gene': 'UBASH3B', 'allele': 'T'},\n", + " {'variant_id': 'rs10883617', 'gene': 'BTRC', 'allele': 'C'},\n", + " {'variant_id': 'rs7149097', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs1910236', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs3744311', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs10489997', 'gene': 'BAZ2B', 'allele': 'T'},\n", + " {'variant_id': 'rs4785367', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs12361312', 'gene': 'MS4A2', 'allele': 'T'},\n", + " {'variant_id': 'rs2550731', 'gene': 'WWOX', 'allele': 'T'},\n", + " {'variant_id': 'rs490332', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs7712169', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs814951', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs306104', 'gene': 'CAMK4', 'allele': 'T'}],\n", + " 'PMC3189752': [{'variant_id': 'SLCO1B1*15',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*15/*15'},\n", + " {'variant_id': 'rs2306283', 'gene': 'SLCO1B1', 'allele': 'GG'}],\n", + " 'PMC1728095': [{'variant_id': 'HLA-DRB1*15:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*15:01'}],\n", + " 'PMC4526634': [{'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1933437', 'gene': 'FLT3', 'allele': 'AA'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'TT'},\n", + " {'variant_id': 'rs2305948', 'gene': 'KDR', 'allele': 'CT'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs2305948', 'gene': 'KDR', 'allele': 'T'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'TT'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1933437', 'gene': 'FLT3', 'allele': 'AA'}],\n", + " 'PMC4941738': [{'variant_id': 'rs1414334', 'gene': 'HTR2C', 'allele': 'C'}],\n", + " 'PMC4068832': [{'variant_id': 'rs3754446', 'gene': 'GSTM1', 'allele': 'C'},\n", + " {'variant_id': 'rs4148405', 'gene': 'ABCC3', 'allele': 'G'},\n", + " {'variant_id': 'rs3754446', 'gene': 'GSTM1', 'allele': 'C'}],\n", + " 'PMC3117061': [{'variant_id': 'rs1799972', 'gene': 'OPRM1', 'allele': 'TT'}],\n", + " 'PMC3663896': [{'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'A'}],\n", + " 'PMC5775044': [{'variant_id': 'CYP2C19 poor metabolizer and intermediate metabolizer genotypes',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC3900431': [{'variant_id': 'NAT2 slow acetylator',\n", + " 'gene': 'NAT2',\n", + " 'allele': None},\n", + " {'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': None},\n", + " {'variant_id': 'GSTT1 null', 'gene': 'GSTT1', 'allele': None},\n", + " {'variant_id': 'rs1799929', 'gene': 'NAT2', 'allele': 'CT'},\n", + " {'variant_id': 'rs2031920', 'gene': 'CYP2E1', 'allele': 'CT'}],\n", + " 'PMC2774340': [{'variant_id': 'HLA-C*03:02',\n", + " 'gene': 'HLA-C',\n", + " 'allele': '*03:02'},\n", + " {'variant_id': 'HLA-C*04:01', 'gene': 'HLA-C', 'allele': '*04:01'}],\n", + " 'PMC3435140': [{'variant_id': 'rs113993960',\n", + " 'gene': 'CFTR',\n", + " 'allele': 'del/del'}],\n", + " 'PMC6200138': [{'variant_id': 'rs45460698', 'gene': 'HTR3B', 'allele': 'del'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AG'}],\n", + " 'PMC3248259': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *2/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *2/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *2/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *2/*3'}],\n", + " 'PMC4958958': [{'variant_id': 'TPMT*1, TPMT*3A',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*3A'},\n", + " {'variant_id': 'TPMT*1, TPMT*3A', 'gene': 'TPMT', 'allele': '*1/*3A'},\n", + " {'variant_id': 'rs2413739', 'gene': 'PACSIN2', 'allele': 'TT'},\n", + " {'variant_id': 'rs10948059', 'gene': 'GNMT', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs10948059', 'gene': 'GNMT', 'allele': 'CT + TT'}],\n", + " 'PMC3387531': [{'variant_id': 'HLA-B*35:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*35:01'},\n", + " {'variant_id': 'HLA-DRB1*01:01', 'gene': 'HLA-DRB1', 'allele': '*01:01'},\n", + " {'variant_id': 'HLA-C*04:01', 'gene': 'HLA-C', 'allele': '*04:01'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs2054675', 'gene': 'CYP2B6', 'allele': 'C'},\n", + " {'variant_id': 'rs3786547', 'gene': 'CYP2B6', 'allele': 'C'}],\n", + " 'PMC4947582': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'}],\n", + " 'PMC5754284': [{'variant_id': 'UGT1A1*1, UGT1A1*27',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*27'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*1/*6'}],\n", + " 'PMC6275563': [{'variant_id': 'rs920829',\n", + " 'gene': 'TRPA1',\n", + " 'allele': 'CT + TT'}],\n", + " 'PMC4409020': [{'variant_id': 'NAT2 deficiency',\n", + " 'gene': 'NAT2',\n", + " 'allele': None},\n", + " {'variant_id': 'rs9877159', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs1775921', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs1801280', 'gene': 'NAT2', 'allele': 'T'},\n", + " {'variant_id': 'rs117421960', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs1208', 'gene': 'NAT2', 'allele': 'A'}],\n", + " 'PMC5561238': [{'variant_id': 'HLA-B*67:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*67:01'},\n", + " {'variant_id': 'HLA-B*78:01', 'gene': 'HLA-B', 'allele': '*78:01'},\n", + " {'variant_id': 'HLA-B*51:02', 'gene': 'HLA-B', 'allele': '*51:02'},\n", + " {'variant_id': 'HLA-B*56:06', 'gene': 'HLA-B', 'allele': '*56:06'},\n", + " {'variant_id': 'HLA-B*39:01', 'gene': 'HLA-B', 'allele': '*39:01'},\n", + " {'variant_id': 'HLA-C*05:09', 'gene': 'HLA-C', 'allele': '*05:09'},\n", + " {'variant_id': 'HLA-B*15:32', 'gene': 'HLA-B', 'allele': '*15:32'},\n", + " {'variant_id': 'HLA-C*05:01', 'gene': 'HLA-C', 'allele': '*05:01'},\n", + " {'variant_id': 'HLA-B*51:01', 'gene': 'HLA-B', 'allele': '*51:01'},\n", + " {'variant_id': 'HLA-B*15:35', 'gene': 'HLA-B', 'allele': '*15:35'},\n", + " {'variant_id': 'HLA-B*15:12', 'gene': 'HLA-B', 'allele': '*15:12'},\n", + " {'variant_id': 'HLA-DRB1*08:01', 'gene': 'HLA-DRB1', 'allele': '*08:01'},\n", + " {'variant_id': 'HLA-B*39:10', 'gene': 'HLA-B', 'allele': '*39:10'},\n", + " {'variant_id': 'HLA-B*38:02', 'gene': 'HLA-B', 'allele': '*38:02'},\n", + " {'variant_id': 'HLA-B*38:01', 'gene': 'HLA-B', 'allele': '*38:01'},\n", + " {'variant_id': 'HLA-DRB1*04:04', 'gene': 'HLA-DRB1', 'allele': '*04:04'},\n", + " {'variant_id': 'HLA-B*56:01', 'gene': 'HLA-B', 'allele': '*56:01'},\n", + " {'variant_id': 'HLA-B*35:05', 'gene': 'HLA-B', 'allele': '*35:05'},\n", + " {'variant_id': 'HLA-DRB1*01:02', 'gene': 'HLA-DRB1', 'allele': '*01:02'},\n", + " {'variant_id': 'HLA-B*39:09', 'gene': 'HLA-B', 'allele': '*39:09'},\n", + " {'variant_id': 'HLA-B*15:01', 'gene': 'HLA-B', 'allele': '*15:01'},\n", + " {'variant_id': 'HLA-B*15:27', 'gene': 'HLA-B', 'allele': '*15:27'},\n", + " {'variant_id': 'HLA-B*35:10', 'gene': 'HLA-B', 'allele': '*35:10'},\n", + " {'variant_id': 'HLA-C*04:06', 'gene': 'HLA-C', 'allele': '*04:06'},\n", + " {'variant_id': 'HLA-B*57:01', 'gene': 'HLA-B', 'allele': '*57:01'},\n", + " {'variant_id': 'HLA-C*04:07', 'gene': 'HLA-C', 'allele': '*04:07'},\n", + " {'variant_id': 'HLA-B*55:02', 'gene': 'HLA-B', 'allele': '*55:02'},\n", + " {'variant_id': 'HLA-DRB1*01:03', 'gene': 'HLA-DRB1', 'allele': '*01:03'},\n", + " {'variant_id': 'HLA-B*55:01', 'gene': 'HLA-B', 'allele': '*55:01'},\n", + " {'variant_id': 'HLA-B*13:02', 'gene': 'HLA-B', 'allele': '*13:02'},\n", + " {'variant_id': 'HLA-B*15:25', 'gene': 'HLA-B', 'allele': '*15:25'},\n", + " {'variant_id': 'HLA-B*39:06', 'gene': 'HLA-B', 'allele': '*39:06'},\n", + " {'variant_id': 'HLA-C*04:03', 'gene': 'HLA-C', 'allele': '*04:03'},\n", + " {'variant_id': 'HLA-B*54:01', 'gene': 'HLA-B', 'allele': '*54:01'},\n", + " {'variant_id': 'HLA-DRB1*01:01', 'gene': 'HLA-DRB1', 'allele': '*01:01'},\n", + " {'variant_id': 'HLA-B*52:01', 'gene': 'HLA-B', 'allele': '*52:01'},\n", + " {'variant_id': 'HLA-DRB1*10:01', 'gene': 'HLA-DRB1', 'allele': '*10:01'},\n", + " {'variant_id': 'HLA-C*04:01', 'gene': 'HLA-C', 'allele': '*04:01'},\n", + " {'variant_id': 'HLA-B*39:05', 'gene': 'HLA-B', 'allele': '*39:05'},\n", + " {'variant_id': 'HLA-C*18:01', 'gene': 'HLA-C', 'allele': '*18:01'},\n", + " {'variant_id': 'HLA-B*15:24', 'gene': 'HLA-B', 'allele': '*15:24'},\n", + " {'variant_id': 'rs28399499', 'gene': 'CYP2B6', 'allele': 'C'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'}],\n", + " 'PMC6511989': [{'variant_id': 'HLA-DQB1*06:02',\n", + " 'gene': 'HLA-DQB1',\n", + " 'allele': '*06:02'},\n", + " {'variant_id': 'HLA-C*06:02', 'gene': 'HLA-C', 'allele': '*06:02'},\n", + " {'variant_id': 'HLA-B*57:01', 'gene': 'HLA-B', 'allele': '*57:01'},\n", + " {'variant_id': 'HLA-DQB1*03:03', 'gene': 'HLA-DQB1', 'allele': '*03:03'},\n", + " {'variant_id': 'HLA-DRB1*15:01', 'gene': 'HLA-DRB1', 'allele': '*15:01'},\n", + " {'variant_id': 'rs72631546', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs2476601', 'gene': 'PTPN22', 'allele': 'A'},\n", + " {'variant_id': 'rs3129880', 'gene': 'HLA-DRA', 'allele': 'T'}],\n", + " 'PMC4190127': [{'variant_id': 'rs1615111', 'gene': 'AREG', 'allele': 'A'}],\n", + " 'PMC6175041': [{'variant_id': 'rs45445694',\n", + " 'gene': 'C18orf56, TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2/(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'}],\n", + " 'PMC7902344': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs12248560', 'gene': 'CYP2C19', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs12248560', 'gene': 'CYP2C19', 'allele': 'CT + TT'}],\n", + " 'PMC5763654': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'}],\n", + " 'PMC6498256': [{'variant_id': 'DPYD deficiency',\n", + " 'gene': 'DPYD',\n", + " 'allele': None}],\n", + " 'PMC5810290': [{'variant_id': 'CYP2D6*1, CYP2D6*4, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4 + *41'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*1xN', 'gene': 'CYP2D6', 'allele': '*1xN'}],\n", + " 'PMC4868001': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *2/*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*1/*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *2/*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*1/*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*1/*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3 + *2/*3'}],\n", + " 'PMC4980919': [{'variant_id': 'rs5853517',\n", + " 'gene': 'MED12L, P2RY12',\n", + " 'allele': 'T/del'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'rs10935838', 'gene': 'MED12L, P2RY12', 'allele': 'AG'},\n", + " {'variant_id': 'rs2046934', 'gene': 'MED12L, P2RY12', 'allele': 'AG'},\n", + " {'variant_id': 'rs6809699', 'gene': 'MED12L, P2RY12', 'allele': 'AC'}],\n", + " 'PMC2362417': [{'variant_id': 'DPYD deficiency',\n", + " 'gene': 'DPYD',\n", + " 'allele': None}],\n", + " 'PMC5367948': [{'variant_id': 'HLA-A*33:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*33:01'},\n", + " {'variant_id': 'rs116561224', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs114577328', 'gene': None, 'allele': 'C'}],\n", + " 'PMC3596139': [{'variant_id': 'CYP2D6*2, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*2/*10'}],\n", + " 'PMC3752814': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None},\n", + " {'variant_id': 'G6PD deficiency', 'gene': 'G6PD', 'allele': None}],\n", + " 'PMC6816974': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*3', 'gene': 'CYP2C19', 'allele': '*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*3', 'gene': 'CYP2C19', 'allele': '*3'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'}],\n", + " 'PMC3716306': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'}],\n", + " 'PMC3808620': [{'variant_id': 'G6PD A- 202A_376G',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'},\n", + " {'variant_id': 'G6PD A- 202A_376G',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'},\n", + " {'variant_id': 'G6PD A- 202A_376G',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'},\n", + " {'variant_id': 'G6PD B (reference), G6PD Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham',\n", + " 'gene': 'G6PD',\n", + " 'allele': \"Mediterranean, Dallas, Panama' Sassari, Cagliari, Birmingham\"},\n", + " {'variant_id': 'G6PD A- 202A_376G',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'},\n", + " {'variant_id': 'G6PD A- 202A_376G',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'},\n", + " {'variant_id': 'G6PD A- 202A_376G',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'},\n", + " {'variant_id': 'G6PD A- 202A_376G',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'},\n", + " {'variant_id': 'G6PD A- 202A_376G',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'},\n", + " {'variant_id': 'G6PD A- 202A_376G, G6PD B (reference)',\n", + " 'gene': 'G6PD',\n", + " 'allele': 'A- 202A_376G'}],\n", + " 'PMC4879695': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs1801265', 'gene': 'DPYD', 'allele': 'G'},\n", + " {'variant_id': 'rs1801160', 'gene': 'DPYD', 'allele': 'T'},\n", + " {'variant_id': 'rs1801159', 'gene': 'DPYD', 'allele': 'C'},\n", + " {'variant_id': 'rs2297595', 'gene': 'DPYD', 'allele': 'C'},\n", + " {'variant_id': 'rs1801158', 'gene': 'DPYD', 'allele': 'T'},\n", + " {'variant_id': 'rs56038477', 'gene': 'DPYD', 'allele': 'T'},\n", + " {'variant_id': 'rs11280056', 'gene': 'TYMS', 'allele': 'TTAAAGTTA'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'T'}],\n", + " 'PMC10657898': [{'variant_id': 'rs28399433',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': 'CC'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'CC'},\n", + " {'variant_id': 'CYP2A6*4', 'gene': 'CYP2A6', 'allele': '*4'},\n", + " {'variant_id': 'rs2622604', 'gene': 'ABCG2', 'allele': 'TT'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'AC'}],\n", + " 'PMC4702374': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'T'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*3/*3 + *1/*3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'CT + TT'}],\n", + " 'PMC7536643': [{'variant_id': 'HLA-B*55:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*55:01'}],\n", + " 'PMC4745369': [{'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*4, CYP2D6*5, CYP2D6*10, CYP2D6*17, CYP2D6*29, CYP2D6*35, CYP2D6*41, CYP2D6*43',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4 + *5/*10 + *5/*41'}],\n", + " 'PMC3894627': [{'variant_id': 'CYP2C9*1',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'rs2359612', 'gene': 'VKORC1', 'allele': 'AA'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'TT'},\n", + " {'variant_id': 'rs2884737', 'gene': 'VKORC1', 'allele': 'CC'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'AA'},\n", + " {'variant_id': 'rs8050894', 'gene': 'VKORC1', 'allele': 'GG'},\n", + " {'variant_id': 'CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'rs2359612', 'gene': 'VKORC1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs8050894', 'gene': 'VKORC1', 'allele': 'CG + GG'},\n", + " {'variant_id': 'rs2884737', 'gene': 'VKORC1', 'allele': 'AC + CC'}],\n", + " 'PMC2291274': [{'variant_id': 'CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'}],\n", + " 'PMC3727245': [{'variant_id': 'CYP2C8*1, CYP2C8*3',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': '*1/*3 + *3/*3'},\n", + " {'variant_id': 'CYP2C8*1, CYP2C8*3',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': '*1/*3 + *3/*3'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs1056836', 'gene': 'CYP1B1', 'allele': 'CC + CG'},\n", + " {'variant_id': 'CYP2C8*1, CYP2C8*3',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': '*1/*3 + *3/*3'}],\n", + " 'PMC3377663': [{'variant_id': 'CYP2C19*1, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'T'},\n", + " {'variant_id': 'rs854560', 'gene': 'PON1', 'allele': 'A'},\n", + " {'variant_id': 'rs705379', 'gene': 'PON1', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs3213619', 'gene': 'ABCB1', 'allele': 'G'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'A'}],\n", + " 'PMC3566726': [{'variant_id': 'rs3836790',\n", + " 'gene': 'SLC6A3',\n", + " 'allele': 'CACATACCATGCAACATACACACTCAGACA/CACATACCATGCAACATACACACTCAGACA'},\n", + " {'variant_id': 'rs6347', 'gene': 'SLC6A3', 'allele': 'TT'},\n", + " {'variant_id': 'rs1076560', 'gene': 'DRD2', 'allele': 'A'},\n", + " {'variant_id': 'rs2283265', 'gene': 'DRD2', 'allele': 'A'}],\n", + " 'PMC2014334': [{'variant_id': 'CYP2C9*1',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2C9*1', 'gene': 'CYP2C9', 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2C9*1', 'gene': 'CYP2C9', 'allele': '*1/*1'}],\n", + " 'PMC5820983': [{'variant_id': 'rs3758581', 'gene': 'CYP2C19', 'allele': 'G'}],\n", + " 'PMC3703235': [{'variant_id': 'TPMT*1, TPMT*3A',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*3A'},\n", + " {'variant_id': 'TPMT*1, TPMT*3A', 'gene': 'TPMT', 'allele': '*1/*3A'},\n", + " {'variant_id': 'TPMT*1, TPMT*33', 'gene': 'TPMT', 'allele': '*1/*33'},\n", + " {'variant_id': 'TPMT*1, TPMT*34', 'gene': 'TPMT', 'allele': '*1/*34'},\n", + " {'variant_id': 'TPMT*1, TPMT*21', 'gene': 'TPMT', 'allele': '*1/*21'},\n", + " {'variant_id': 'TPMT*1, TPMT*2, TPMT*3A, TPMT*3C',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*2 + *1/*3A + *1/*3C'},\n", + " {'variant_id': 'TPMT*1, TPMT*9, TPMT*21, TPMT*32, TPMT*33, TPMT*34',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*9 + *1/*21 + *1/*32 + *1/*33 + *1/*34'},\n", + " {'variant_id': 'TPMT*1, TPMT*32', 'gene': 'TPMT', 'allele': '*1/*32'}],\n", + " 'PMC1855340': [{'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'rs8050894', 'gene': 'VKORC1', 'allele': 'G'}],\n", + " 'PMC3718044': [{'variant_id': 'CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'},\n", + " {'variant_id': 'rs8050894', 'gene': 'VKORC1', 'allele': 'G'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'}],\n", + " 'PMC7098961': [{'variant_id': 'rs73032311', 'gene': 'CYP2A7', 'allele': 'C'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'A'},\n", + " {'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'C'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'T'}],\n", + " 'PMC3763712': [{'variant_id': 'CYP2B6*18', 'gene': 'CYP2B6', 'allele': '*18'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*6', 'gene': 'CYP2B6', 'allele': '*6'}],\n", + " 'PMC2935998': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2xN',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*2xN'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2xN',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*2xN'}],\n", + " 'PMC11728264': [{'variant_id': 'rs4570625', 'gene': 'TPH2', 'allele': 'TT'},\n", + " {'variant_id': 'rs7305115', 'gene': 'TPH2', 'allele': 'GG'},\n", + " {'variant_id': 'rs1386493', 'gene': 'TPH2', 'allele': 'GG'}],\n", + " 'PMC3193709': [{'variant_id': 'CYP2C9*1, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*3'}],\n", + " 'PMC1222557': [{'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs568132506', 'gene': 'DPYD', 'allele': 'AA'},\n", + " {'variant_id': 'rs72549305', 'gene': 'DPYD', 'allele': 'CC'},\n", + " {'variant_id': 'rs72549307', 'gene': 'DPYD', 'allele': 'CC'},\n", + " {'variant_id': 'rs72549308', 'gene': 'DPYD', 'allele': 'GT'},\n", + " {'variant_id': 'rs72547601', 'gene': 'DPYD', 'allele': 'CC'},\n", + " {'variant_id': 'rs72549304', 'gene': 'DPYD', 'allele': 'AA'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs55886062', 'gene': 'DPYD', 'allele': 'AC'}],\n", + " 'PMC6429833': [{'variant_id': 'UGT1A1 deficiency',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': None},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC1755602': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTCGCCTGCCTCCGTCCCG)3'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2372536', 'gene': 'ATIC', 'allele': 'G'}],\n", + " 'PMC3605700': [{'variant_id': 'rs2125739', 'gene': 'ABCC10', 'allele': 'CC'}],\n", + " 'PMC2564574': [{'variant_id': 'rs2884737', 'gene': 'VKORC1', 'allele': 'C'},\n", + " {'variant_id': 'rs8050894', 'gene': 'VKORC1', 'allele': 'G'},\n", + " {'variant_id': 'rs2359612', 'gene': 'VKORC1', 'allele': 'G'},\n", + " {'variant_id': 'rs17708472', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs7294', 'gene': 'PRSS53, VKORC1', 'allele': 'T'},\n", + " {'variant_id': 'CYP2C9*2', 'gene': 'CYP2C9', 'allele': '*2'},\n", + " {'variant_id': 'CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*3'}],\n", + " 'PMC3550197': [{'variant_id': 'CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'}],\n", + " 'PMC3820275': [{'variant_id': 'CYP2A6*1, CYP2A6*2, CYP2A6*9, CYP2A6*17, CYP2A6*20',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': '*1/*1'}],\n", + " 'PMC3862586': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *2/*2'},\n", + " {'variant_id': 'rs2134688', 'gene': 'ARNT', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4823613', 'gene': 'PPARA', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs4253728', 'gene': 'PPARA', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs35599367', 'gene': 'CYP3A4', 'allele': 'AG'}],\n", + " 'PMC4752331': [{'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'C'},\n", + " {'variant_id': 'rs2046934', 'gene': 'P2RY12', 'allele': 'A'},\n", + " {'variant_id': 'rs1330344', 'gene': 'PTGS1', 'allele': 'T'}],\n", + " 'PMC2014382': [{'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3/*3 + *3/*4 + *4/*4'}],\n", + " 'PMC2887732': [{'variant_id': 'SLC6A4 HTTLPR short form (S allele), SLC6A4 L allele-rs25531C, SLC6A4 L allele-rs25531T',\n", + " 'gene': 'SLC6A4',\n", + " 'allele': 'HTTLPR short form (S allele)/L allele-rs25531C'},\n", + " {'variant_id': 'rs25531', 'gene': 'SLC6A4', 'allele': 'C'}],\n", + " 'PMC5127810': [{'variant_id': 'rs7438135', 'gene': 'UGT2B7', 'allele': 'G'},\n", + " {'variant_id': 'rs11572076', 'gene': 'CYP2C8', 'allele': 'CC'}],\n", + " 'PMC3482956': [{'variant_id': 'rs2804402', 'gene': 'ABCC2', 'allele': 'G'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs8187710', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs1885301', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs8187710', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs8187710', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs17216177', 'gene': 'ABCC2', 'allele': 'C'},\n", + " {'variant_id': 'rs1885301', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs17216177', 'gene': 'ABCC2', 'allele': 'C'},\n", + " {'variant_id': 'rs2804402', 'gene': 'ABCC2', 'allele': 'G'},\n", + " {'variant_id': 'rs1885301', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'A'},\n", + " {'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs17216177', 'gene': 'ABCC2', 'allele': 'C'}],\n", + " 'PMC3057982': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4'}],\n", + " 'PMC4180737': [{'variant_id': 'HLA-DQB1*06:02',\n", + " 'gene': 'HLA-DQB1',\n", + " 'allele': '*06:02'}],\n", + " 'PMC8444616': [{'variant_id': 'rs1801265',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'AG + GG'}],\n", + " 'PMC4833149': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*1/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*1/*3'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3'}],\n", + " 'PMC3384479': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'CYP2C9*2, CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'TT'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'CC'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'TT'},\n", + " {'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2 + *3'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'CC'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'}],\n", + " 'PMC4937762': [{'variant_id': 'HLA-B*57:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*57:01'}],\n", + " 'PMC9024670': [{'variant_id': 'rs3764435', 'gene': 'ALDH1A1', 'allele': 'AA'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'GG'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'TT'}],\n", + " 'PMC6563124': [{'variant_id': 'rs1225367412',\n", + " 'gene': 'CACNA1S',\n", + " 'allele': 'del'},\n", + " {'variant_id': 'rs193922826', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs1204964501', 'gene': 'CACNA1S', 'allele': 'G'},\n", + " {'variant_id': 'rs141678782', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs757284447', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs111364296', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs529038948', 'gene': 'CACNA1S', 'allele': 'A'},\n", + " {'variant_id': 'rs768445692', 'gene': 'CACNA1S', 'allele': 'C'},\n", + " {'variant_id': 'rs377474103', 'gene': 'CACNA1S', 'allele': 'C'}],\n", + " 'PMC5876888': [{'variant_id': 'HLA-B*13:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*13:01'}],\n", + " 'PMC7156285': [{'variant_id': 'HLA-A*31:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*31:01'},\n", + " {'variant_id': 'HLA-A*31:01', 'gene': 'HLA-A', 'allele': '*31:01'},\n", + " {'variant_id': 'rs192543598', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs187926838', 'gene': 'ALK', 'allele': 'G'}],\n", + " 'PMC2957368': [{'variant_id': 'rs1799752',\n", + " 'gene': 'ACE',\n", + " 'allele': 'ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC/ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC + ATACAGTCACTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCC/del'},\n", + " {'variant_id': 'rs4291', 'gene': 'ACE', 'allele': 'AA + AT'}],\n", + " 'PMC10399933': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2/*2 + *2/*3 + *3/*3'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC + CT'}],\n", + " 'PMC10374328': [{'variant_id': 'HLA-B*15:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02'}],\n", + " 'PMC10366597': [{'variant_id': 'CYP2D6*4, CYP2D6*5, CYP2D6*68',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4 + *5 + *68'},\n", + " {'variant_id': 'CYP2D6*4, CYP2D6*68',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4 + *68'}],\n", + " 'PMC8375217': [{'variant_id': 'CYP2D6*41', 'gene': 'CYP2D6', 'allele': '*41'},\n", + " {'variant_id': 'CYP2D6*41', 'gene': 'CYP2D6', 'allele': '*41'},\n", + " {'variant_id': 'CYP2D6*41', 'gene': 'CYP2D6', 'allele': '*41'}],\n", + " 'PMC10398775': [{'variant_id': 'HLA-A*31:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*31:01'}],\n", + " 'PMC10381559': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC10404721': [{'variant_id': 'HLA-B*13:01, HLA-B*13:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*13:01 + *13:02'}],\n", + " 'PMC6713716': [{'variant_id': 'CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4'}],\n", + " 'PMC10841235': [{'variant_id': 'HLA-A*03:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*03:01'},\n", + " {'variant_id': 'HLA-A*32:01', 'gene': 'HLA-A', 'allele': '*32:01'},\n", + " {'variant_id': 'HLA-B*53:01', 'gene': 'HLA-B', 'allele': '*53:01'}],\n", + " 'PMC10443690': [{'variant_id': 'CYP2C19*1',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*1'}],\n", + " 'PMC8100096': [{'variant_id': 'HLA-DRB1*10:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*10:01'},\n", + " {'variant_id': 'rs71542416', 'gene': None, 'allele': 'T'}],\n", + " 'PMC10582682': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*8, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3 + *8'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*1 + *1/*17 + *17/*17'},\n", + " {'variant_id': 'CYP2C19*2, CYP2C19*3, CYP2C19*8',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3 + *8'}],\n", + " 'PMC10789960': [{'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10 + *1/*10'}],\n", + " 'PMC10448185': [{'variant_id': 'CYP2D6*5',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*5/*5'}],\n", + " 'PMC10522553': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4'}],\n", + " 'PMC10494815': [{'variant_id': 'CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*3'}],\n", + " 'PMC10486269': [{'variant_id': 'CYP2C19 intermediate metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 intermediate metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC10501134': [{'variant_id': 'NAT2 slow acetylator',\n", + " 'gene': 'NAT2',\n", + " 'allele': None},\n", + " {'variant_id': 'NAT2*4, NAT2*6, NAT2*7, NAT2*16',\n", + " 'gene': 'NAT2',\n", + " 'allele': '*6/*6 + *6/*7 + *7/*7'}],\n", + " 'PMC10885131': [{'variant_id': 'HLA-A*32:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*32:01'}],\n", + " 'PMC10546186': [{'variant_id': 'HLA-A*02:07',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*02:07'},\n", + " {'variant_id': 'HLA-A*01:01', 'gene': 'HLA-A', 'allele': '*01:01'},\n", + " {'variant_id': 'HLA-C*06:02', 'gene': 'HLA-C', 'allele': '*06:02'},\n", + " {'variant_id': 'HLA-DRB1*15:01', 'gene': 'HLA-DRB1', 'allele': '*15:01'},\n", + " {'variant_id': 'HLA-DQB1*03:02', 'gene': 'HLA-DQB1', 'allele': '*03:02'}],\n", + " 'PMC10550831': [{'variant_id': 'CYP2C19 poor metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC10926675': [{'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'AC'},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'CC'}],\n", + " 'PMC10618803': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3'}],\n", + " 'PMC10732300': [{'variant_id': 'CYP2B6 poor metabolizer',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': None}],\n", + " 'PMC3638364': [{'variant_id': 'rs1152003', 'gene': None, 'allele': 'CC'}],\n", + " 'PMC10633914': [{'variant_id': 'rs143004875',\n", + " 'gene': 'DPYS',\n", + " 'allele': '(T)7/(T)8'},\n", + " {'variant_id': 'rs2016520', 'gene': 'PPARD', 'allele': 'T'}],\n", + " 'PMC10487921': [{'variant_id': 'CYP2D6*1, CYP2D6*3, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*3 + *4'}],\n", + " 'PMC3235084': [{'variant_id': 'rs2278293', 'gene': 'IMPDH1', 'allele': 'T'},\n", + " {'variant_id': 'rs2278294', 'gene': 'IMPDH1', 'allele': 'T'},\n", + " {'variant_id': 'rs2278294', 'gene': 'IMPDH1', 'allele': 'T'},\n", + " {'variant_id': 'rs4974081', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs11706052', 'gene': 'IMPDH2', 'allele': 'G'},\n", + " {'variant_id': 'rs2278293', 'gene': 'IMPDH1', 'allele': 'T'}],\n", + " 'PMC2883760': [{'variant_id': 'rs11692021', 'gene': None, 'allele': 'CC'},\n", + " {'variant_id': 'rs1042597', 'gene': 'UGT1A8', 'allele': 'CC'},\n", + " {'variant_id': 'rs1042597', 'gene': 'UGT1A8', 'allele': 'CC'},\n", + " {'variant_id': 'rs17863762', 'gene': 'UGT1A8', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs6714486', 'gene': 'UGT1A9', 'allele': 'AA + AT'},\n", + " {'variant_id': 'rs1042597', 'gene': 'UGT1A8', 'allele': 'CC'}],\n", + " 'PMC10947784': [{'variant_id': 'DPYD deficiency',\n", + " 'gene': 'DPYD',\n", + " 'allele': None}],\n", + " 'PMC10690766': [{'variant_id': 'CYP3A5*1, CYP3A5*3, CYP3A5*6, CYP3A5*7',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*3/*3 + *3/*6 + *3/*7'},\n", + " {'variant_id': 'rs3832526',\n", + " 'gene': 'ASNS',\n", + " 'allele': '(GCGGGGCGCAGGGC)3/(GCGGGGCGCAGGGC)3'},\n", + " {'variant_id': 'rs1049674', 'gene': 'ASNS', 'allele': 'AT'},\n", + " {'variant_id': 'rs9282564', 'gene': 'ABCB1', 'allele': 'CT'}],\n", + " 'PMC10932604': [{'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'G'}],\n", + " 'PMC2853447': [{'variant_id': 'rs17655652', 'gene': 'NPC1L1', 'allele': 'CC'},\n", + " {'variant_id': 'rs17655652', 'gene': 'NPC1L1', 'allele': 'CC'}],\n", + " 'PMC10739331': [{'variant_id': 'rs11280056',\n", + " 'gene': 'TYMS',\n", + " 'allele': 'TTA/TTAAAGTTA'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'GT'}],\n", + " 'PMC11889249': [{'variant_id': 'CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'}],\n", + " 'PMC10787143': [{'variant_id': 'CYP2D6 intermediate metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2B6 poor metabolizer', 'gene': 'CYP2B6', 'allele': None},\n", + " {'variant_id': 'CYP3A4 poor metabolizer', 'gene': 'CYP3A4', 'allele': None},\n", + " {'variant_id': 'CYP2D6 ultrarapid metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC10740827': [{'variant_id': 'CYP2C19 poor metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC10741656': [{'variant_id': 'HLA-A*02:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*02:01'},\n", + " {'variant_id': 'HLA-A*34:01', 'gene': 'HLA-A', 'allele': '*34:01'},\n", + " {'variant_id': 'HLA-DRB1*16:02', 'gene': 'HLA-DRB1', 'allele': '*16:02'}],\n", + " 'PMC11194867': [{'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'GG'}],\n", + " 'PMC10800424': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'}],\n", + " 'PMC10814806': [{'variant_id': 'rs11280056',\n", + " 'gene': 'TYMS',\n", + " 'allele': 'del/del'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'GG'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs113488022', 'gene': 'BRAF', 'allele': 'T'},\n", + " {'variant_id': 'rs1801265', 'gene': 'DPYD', 'allele': 'AG + GG'}],\n", + " 'PMC1735714': [{'variant_id': 'rs121918594', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs118192172', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs63749869', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs118192124', 'gene': 'RYR1', 'allele': 'T'}],\n", + " 'PMC1887589': [{'variant_id': 'rs446112', 'gene': None, 'allele': 'AA'},\n", + " {'variant_id': 'rs2784917', 'gene': 'SLIT1', 'allele': 'AA'},\n", + " {'variant_id': 'rs6539870', 'gene': None, 'allele': 'GG'},\n", + " {'variant_id': 'rs2784917', 'gene': None, 'allele': 'AA'},\n", + " {'variant_id': 'rs6539870', 'gene': None, 'allele': 'GG'},\n", + " {'variant_id': 'rs446112', 'gene': None, 'allele': 'AA'}],\n", + " 'PMC8490297': [{'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs45445694',\n", + " 'gene': 'TYMS',\n", + " 'allele': '(CCGCGCCACTTGGCCTGCCTCCGTCCCG)2'},\n", + " {'variant_id': 'rs25487', 'gene': 'XRCC1', 'allele': 'T'},\n", + " {'variant_id': 'rs25487', 'gene': 'XRCC1', 'allele': 'T'},\n", + " {'variant_id': 'rs7439366', 'gene': 'UGT2B7', 'allele': 'T'},\n", + " {'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'C'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'A'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'A'},\n", + " {'variant_id': 'rs7439366', 'gene': 'UGT2B7', 'allele': 'T'},\n", + " {'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'C'},\n", + " {'variant_id': 'rs7439366', 'gene': 'UGT2B7', 'allele': 'T'},\n", + " {'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'C'},\n", + " {'variant_id': 'rs25487', 'gene': 'XRCC1', 'allele': 'T'},\n", + " {'variant_id': 'rs25487', 'gene': 'XRCC1', 'allele': 'T'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'A'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'A'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'A'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'A'},\n", + " {'variant_id': 'rs7439366', 'gene': 'UGT2B7', 'allele': 'T'},\n", + " {'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'C'},\n", + " {'variant_id': 'rs11615', 'gene': 'ERCC1', 'allele': 'A'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'A'}],\n", + " 'PMC4283622': [{'variant_id': 'rs1801265',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs777425216', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs55886062', 'gene': 'DPYD', 'allele': 'AC'},\n", + " {'variant_id': 'rs1801159', 'gene': 'DPYD', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs1801160', 'gene': 'DPYD', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs45589337', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs2297595', 'gene': 'DPYD', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs1801158', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs56038477', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs12022243', 'gene': 'DPYD', 'allele': 'T'},\n", + " {'variant_id': 'rs7548189', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs2741171', 'gene': 'ENOSF1', 'allele': 'T'},\n", + " {'variant_id': 'rs2612091', 'gene': 'ENOSF1', 'allele': 'C'},\n", + " {'variant_id': 'rs12022243', 'gene': 'DPYD', 'allele': 'T'},\n", + " {'variant_id': 'rs2741171', 'gene': 'ENOSF1', 'allele': 'T'},\n", + " {'variant_id': 'rs2612091', 'gene': 'ENOSF1', 'allele': 'C'},\n", + " {'variant_id': 'rs7548189', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs7548189', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs2741171', 'gene': 'ENOSF1', 'allele': 'T'},\n", + " {'variant_id': 'rs2612091', 'gene': 'ENOSF1', 'allele': 'C'},\n", + " {'variant_id': 'rs12022243', 'gene': 'DPYD', 'allele': 'T'},\n", + " {'variant_id': 'rs76387818', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs12132152', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs76387818', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs12132152', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs76387818', 'gene': 'DPYD', 'allele': 'A'},\n", + " {'variant_id': 'rs12132152', 'gene': 'DPYD', 'allele': 'A'}],\n", + " 'PMC3165308': [{'variant_id': 'rs4149032', 'gene': 'SLCO1B1', 'allele': 'T'}],\n", + " 'PMC9531708': [{'variant_id': 'rs2228478', 'gene': 'MC1R', 'allele': 'G'},\n", + " {'variant_id': 'rs2228479', 'gene': 'MC1R', 'allele': 'A'}],\n", + " 'PMC2966859': [{'variant_id': 'rs9394992', 'gene': 'SLC29A1', 'allele': 'CC'},\n", + " {'variant_id': 'rs8187758', 'gene': 'SLC28A1', 'allele': 'CC'},\n", + " {'variant_id': 'rs1060896', 'gene': 'SLC28A2', 'allele': 'AA'},\n", + " {'variant_id': 'rs7853758', 'gene': 'SLC28A3', 'allele': 'GG'},\n", + " {'variant_id': 'rs7867504', 'gene': 'SLC28A3', 'allele': 'CC'},\n", + " {'variant_id': 'rs760370', 'gene': 'SLC29A1', 'allele': 'GG'},\n", + " {'variant_id': 'rs324148', 'gene': 'SLC29A1', 'allele': 'CC'},\n", + " {'variant_id': 'rs12648166', 'gene': 'DCK', 'allele': 'AA'},\n", + " {'variant_id': 'rs9937', 'gene': 'RRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1042858', 'gene': 'RRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs183484', 'gene': 'RRM1', 'allele': 'AC'},\n", + " {'variant_id': 'rs2242048', 'gene': 'SLC28A1', 'allele': 'GG'},\n", + " {'variant_id': 'rs4742', 'gene': 'DCTD', 'allele': 'GG'},\n", + " {'variant_id': 'rs1048977', 'gene': 'CDA', 'allele': 'CC'},\n", + " {'variant_id': 'rs2072671', 'gene': 'CDA', 'allele': 'AA'},\n", + " {'variant_id': 'rs4694362', 'gene': 'DCK', 'allele': 'TT'},\n", + " {'variant_id': 'rs9394992', 'gene': 'SLC29A1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs9937', 'gene': 'RRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs2072671', 'gene': 'CDA', 'allele': 'AA'},\n", + " {'variant_id': 'rs4694362', 'gene': 'DCK', 'allele': 'TT'}],\n", + " 'PMC9951409': [{'variant_id': 'rs4986893', 'gene': 'CYP2C19', 'allele': 'A'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'A'},\n", + " {'variant_id': 'rs1463411', 'gene': 'MIR6076', 'allele': 'GG + GT'},\n", + " {'variant_id': 'rs1463411', 'gene': 'MIR6076', 'allele': 'G'}],\n", + " 'PMC5824214': [{'variant_id': 'rs4788863', 'gene': 'SLC16A5', 'allele': 'T'},\n", + " {'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'C'},\n", + " {'variant_id': 'rs10981694', 'gene': 'SLC31A1', 'allele': 'G'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'G'},\n", + " {'variant_id': 'rs1051640', 'gene': 'ABCC3', 'allele': 'G'},\n", + " {'variant_id': 'rs9332377', 'gene': 'COMT', 'allele': 'C'},\n", + " {'variant_id': 'rs4646316', 'gene': 'COMT', 'allele': 'T'},\n", + " {'variant_id': 'rs12201199', 'gene': 'TPMT', 'allele': 'T'},\n", + " {'variant_id': 'rs1800460', 'gene': 'TPMT', 'allele': 'C'},\n", + " {'variant_id': 'rs2228001', 'gene': 'XPC', 'allele': 'G'},\n", + " {'variant_id': 'rs316019', 'gene': 'SLC22A2', 'allele': 'C'}],\n", + " 'PMC5963629': [{'variant_id': 'rs10817465', 'gene': 'SLC31A1', 'allele': 'C'},\n", + " {'variant_id': 'rs10513202', 'gene': 'SLC31A1', 'allele': 'G'},\n", + " {'variant_id': 'rs10759637', 'gene': 'SLC31A1', 'allele': 'AC'},\n", + " {'variant_id': 'rs2233914', 'gene': 'SLC31A1', 'allele': 'AA'},\n", + " {'variant_id': 'rs10817464', 'gene': 'SLC31A1', 'allele': 'T'},\n", + " {'variant_id': 'rs2233914', 'gene': 'SLC31A1', 'allele': 'AA'},\n", + " {'variant_id': 'rs10759637', 'gene': 'SLC31A1', 'allele': 'AC'},\n", + " {'variant_id': 'rs4979223', 'gene': 'SLC31A1', 'allele': 'AC'},\n", + " {'variant_id': 'rs4978536', 'gene': 'SLC31A1', 'allele': 'A'},\n", + " {'variant_id': 'rs4978536', 'gene': 'SLC31A1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs10817464', 'gene': 'SLC31A1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs4979223', 'gene': 'SLC31A1', 'allele': 'AC'}],\n", + " 'PMC4966576': [{'variant_id': 'CYP2C19 poor metabolizers',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC4000704': [{'variant_id': 'rs11868035',\n", + " 'gene': 'SREBF1',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC6969029': [{'variant_id': 'rs12305038', 'gene': 'PDE3A', 'allele': 'A'},\n", + " {'variant_id': 'rs11882256', 'gene': 'MUC16', 'allele': 'T'},\n", + " {'variant_id': 'rs12305038', 'gene': 'PDE3A', 'allele': 'A'},\n", + " {'variant_id': 'rs36024412', 'gene': 'CRYBG2', 'allele': 'T'},\n", + " {'variant_id': 'rs168107', 'gene': 'CLCC1', 'allele': 'T'},\n", + " {'variant_id': 'rs41269255', 'gene': 'POM121L2', 'allele': 'T'},\n", + " {'variant_id': 'rs9393888', 'gene': 'ZNF165', 'allele': 'C'},\n", + " {'variant_id': 'rs10405238', 'gene': 'ZNF568', 'allele': 'G'}],\n", + " 'PMC2121164': [{'variant_id': 'rs1042713', 'gene': 'ADRB2', 'allele': 'AA'}],\n", + " 'PMC11790433': [{'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'AA'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'TT'}],\n", + " 'PMC11293078': [{'variant_id': 'rs1799930', 'gene': 'NAT2', 'allele': 'G'},\n", + " {'variant_id': 'rs1801280', 'gene': 'NAT2', 'allele': 'T'}],\n", + " 'PMC5395804': [{'variant_id': 'rs12979860',\n", + " 'gene': 'IFNL3, IFNL4',\n", + " 'allele': 'CC'}],\n", + " 'PMC3232196': [{'variant_id': 'NAT2*1, NAT2*4, NAT2*6, NAT2*7, NAT2*16',\n", + " 'gene': 'NAT2',\n", + " 'allele': '*16/*16 + *16/*6 + *16/*7 + *6/*6 + *6/*7'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'TT'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'}],\n", + " 'PMC10506906': [{'variant_id': 'SLCO1B1*5',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*5'}],\n", + " 'PMC1894729': [{'variant_id': 'NAT2*5, NAT2*6, NAT2*7, NAT2*16',\n", + " 'gene': 'NAT2',\n", + " 'allele': '*16 + *5 + *6 + *7'}],\n", + " 'PMC10880264': [{'variant_id': 'CYP2C19 intermediate metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizer', 'gene': 'CYP2D6', 'allele': None},\n", + " {'variant_id': 'CYP2C19 intermediate metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'rs6311', 'gene': 'HTR2A', 'allele': 'CT + TT'}],\n", + " 'PMC10984958': [{'variant_id': 'NAT2*4, NAT2*6, NAT2*7',\n", + " 'gene': 'NAT2',\n", + " 'allele': '*6/*6 + *6/*7 + *7/*7'},\n", + " {'variant_id': 'rs1061472', 'gene': 'ATP7B', 'allele': 'CC'}],\n", + " 'PMC10910298': [{'variant_id': 'CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'}],\n", + " 'PMC12009671': [{'variant_id': 'HLA-DRB1*04:03',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*04:03'},\n", + " {'variant_id': 'CYP2C9*2, CYP2C9*3', 'gene': 'CYP2C9', 'allele': '*2 + *3'},\n", + " {'variant_id': 'HLA-B*35:03', 'gene': 'HLA-B', 'allele': '*35:03'}],\n", + " 'PMC10541540': [{'variant_id': 'NAT2*4, NAT2*6, NAT2*7, NAT2*16',\n", + " 'gene': 'NAT2',\n", + " 'allele': '*16/*16 + *16/*6 + *16/*7 + *6/*6 + *6/*7 + *7/*7'},\n", + " {'variant_id': 'rs2031920', 'gene': 'CYP2E1', 'allele': 'C'},\n", + " {'variant_id': 'rs1799931', 'gene': 'NAT2', 'allele': 'A'},\n", + " {'variant_id': 'rs1799930', 'gene': 'NAT2', 'allele': 'A'},\n", + " {'variant_id': 'rs1799929', 'gene': 'NAT2', 'allele': 'T'}],\n", + " 'PMC8426517': [{'variant_id': 'rs1051730', 'gene': 'CHRNA3', 'allele': 'A'},\n", + " {'variant_id': 'rs1051730', 'gene': 'CHRNA3', 'allele': 'AA + AG'}],\n", + " 'PMC6889971': [{'variant_id': 'CYP2C19 poor metabolizer and intermediate metabolizer genotypes',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2C19 rapid and ultrarapid metabolizer genotypes',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC7586356': [{'variant_id': 'CYP2D6 poor metabolizer genotype',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 ultrarapid metabolizer genotype',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC3309132': [{'variant_id': 'CYP2D6 poor metabolizers and intermediate metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizers and intermediate metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC5675690': [{'variant_id': 'rs1128503',\n", + " 'gene': 'ABCB1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs683369', 'gene': 'SLC22A1', 'allele': 'CG + GG'}],\n", + " 'PMC6465603': [{'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'C'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'CT + TT'}],\n", + " 'PMC3858967': [{'variant_id': 'rs871514', 'gene': 'UGT1A4', 'allele': 'C'},\n", + " {'variant_id': 'rs13401281', 'gene': 'UGT1A4', 'allele': 'G'},\n", + " {'variant_id': 'rs4124874', 'gene': 'UGT1A1', 'allele': 'T'},\n", + " {'variant_id': 'rs10929302', 'gene': 'UGT1A1', 'allele': 'A'},\n", + " {'variant_id': 'rs1042597', 'gene': 'UGT1A8', 'allele': 'G'},\n", + " {'variant_id': 'rs1042605', 'gene': 'UGT1A8', 'allele': 'G'},\n", + " {'variant_id': 'rs12468274', 'gene': 'UGT1A4', 'allele': 'C'},\n", + " {'variant_id': 'rs1551285', 'gene': 'UGT1A8', 'allele': 'A'},\n", + " {'variant_id': 'rs2011404', 'gene': 'UGT1A4', 'allele': 'C'},\n", + " {'variant_id': 'rs6755571', 'gene': 'UGT1A4', 'allele': 'AC'},\n", + " {'variant_id': 'rs6431558', 'gene': 'UGT1A8', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs1801030', 'gene': 'SULT1A1, SULT1A2', 'allele': 'C'},\n", + " {'variant_id': 'rs3755319', 'gene': 'UGT1A1', 'allele': 'C'},\n", + " {'variant_id': 'rs62298861', 'gene': 'UGT2B7', 'allele': 'G'},\n", + " {'variant_id': 'rs3760091', 'gene': 'SULT1A1', 'allele': 'G'},\n", + " {'variant_id': 'rs7668258', 'gene': 'UGT2B7', 'allele': 'C'},\n", + " {'variant_id': 'rs750155', 'gene': 'SULT1A1', 'allele': 'T'},\n", + " {'variant_id': 'rs1042028', 'gene': 'SULT1A1', 'allele': 'T'}],\n", + " 'PMC3260990': [{'variant_id': 'rs7164594',\n", + " 'gene': 'HYKK',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs8109525', 'gene': 'CYP2B6', 'allele': 'GG'},\n", + " {'variant_id': 'rs3758987', 'gene': 'HTR3B', 'allele': 'TT'},\n", + " {'variant_id': 'rs11606194', 'gene': 'HTR3B', 'allele': 'CT + TT'}],\n", + " 'PMC10779905': [{'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'GG'}],\n", + " 'PMC3116071': [{'variant_id': 'rs2231164',\n", + " 'gene': 'ABCG2',\n", + " 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2847153', 'gene': 'TYMS', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs2237717', 'gene': 'MET', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1760217', 'gene': 'DPYD', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs17091162', 'gene': 'SERPINA3', 'allele': 'AC + CC'}],\n", + " 'PMC4480333': [{'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2C19 poor metabolizers',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2'}],\n", + " 'PMC5533343': [{'variant_id': 'rs201052613', 'gene': None, 'allele': '(T)6'},\n", + " {'variant_id': 'rs138741635', 'gene': 'FHIT', 'allele': 'A'},\n", + " {'variant_id': 'rs192427471', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs181937009', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs12290663', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs147204125', 'gene': 'HEATR4', 'allele': 'G'},\n", + " {'variant_id': 'rs117714106', 'gene': None, 'allele': 'T'}],\n", + " 'PMC4050976': [{'variant_id': 'rs2245360', 'gene': 'PAH', 'allele': 'AA'}],\n", + " 'PMC5853681': [{'variant_id': 'CYP2B6 poor metabolizer and intermediate metabolizer genotypes',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': None}],\n", + " 'PMC1856436': [{'variant_id': 'TPMT intermediate metabolizers',\n", + " 'gene': 'TPMT',\n", + " 'allele': None},\n", + " {'variant_id': 'TPMT intermediate metabolizers',\n", + " 'gene': 'TPMT',\n", + " 'allele': None},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'C'}],\n", + " 'PMC4959996': [{'variant_id': 'rs4793665', 'gene': 'ABCC3', 'allele': 'CC'},\n", + " {'variant_id': 'rs4148412', 'gene': 'ABCC3', 'allele': 'TT'},\n", + " {'variant_id': 'rs4148412', 'gene': 'ABCC3', 'allele': 'T'},\n", + " {'variant_id': 'rs739923', 'gene': 'ABCC3', 'allele': 'G'},\n", + " {'variant_id': 'rs1978153', 'gene': 'ABCC3', 'allele': 'C'},\n", + " {'variant_id': 'rs733392', 'gene': 'ABCC3', 'allele': 'G'},\n", + " {'variant_id': 'rs886493', 'gene': 'ABCC3', 'allele': 'G'},\n", + " {'variant_id': 'rs7216383', 'gene': 'ABCC3', 'allele': 'T'}],\n", + " 'PMC5396189': [{'variant_id': 'rs25487',\n", + " 'gene': 'XRCC1',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1042927', 'gene': 'RRM1', 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs13181', 'gene': 'ERCC2', 'allele': 'GG + GT'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs2236225', 'gene': 'MTHFD1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs3212986', 'gene': 'ERCC1', 'allele': 'AA + AC'}],\n", + " 'PMC10089949': [{'variant_id': 'CYP3A5 intermediate metabolizer and normal metabolizer',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': None}],\n", + " 'PMC6303239': [{'variant_id': 'rs887829',\n", + " 'gene': 'UGT1A1, UGT1A10, UGT1A3, UGT1A4, UGT1A5, UGT1A6, UGT1A7, UGT1A8, UGT1A9',\n", + " 'allele': 'TT'}],\n", + " 'PMC4599281': [{'variant_id': 'rs2257212',\n", + " 'gene': 'SLC15A2',\n", + " 'allele': 'CC'}],\n", + " 'PMC6631257': [{'variant_id': 'CYP2D6 normal metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 normal metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 ultrarapid metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizers and intermediate metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC3275159': [{'variant_id': 'rs1804645', 'gene': 'NCOA1', 'allele': 'CT'},\n", + " {'variant_id': 'rs1804645', 'gene': 'NCOA1', 'allele': 'CT'},\n", + " {'variant_id': 'rs1804645', 'gene': 'NCOA1', 'allele': 'CT'},\n", + " {'variant_id': 'rs1804645', 'gene': 'NCOA1', 'allele': 'T'},\n", + " {'variant_id': 'rs1804645', 'gene': 'NCOA1', 'allele': 'T'}],\n", + " 'PMC3775934': [{'variant_id': 'rs2869950', 'gene': 'CHRNB4', 'allele': 'T'},\n", + " {'variant_id': 'rs578776', 'gene': 'CHRNA3', 'allele': 'G'},\n", + " {'variant_id': 'rs16969968', 'gene': 'CHRNA5', 'allele': 'G'}],\n", + " 'PMC9504609': [{'variant_id': 'rs1142345',\n", + " 'gene': 'TPMT',\n", + " 'allele': 'CC + CT'}],\n", + " 'PMC3048820': [{'variant_id': 'CYP2C19 intermediate metabolizer genotype',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2C19 intermediate metabolizer genotype',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2C19 poor metabolizer genotype',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2C19 poor metabolizer genotype',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC3784025': [{'variant_id': 'rs352428', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs352428', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs9380524', 'gene': 'FKBP5', 'allele': 'A'}],\n", + " 'PMC2692932': [{'variant_id': 'rs762551', 'gene': 'CYP1A2', 'allele': 'A'}],\n", + " 'PMC10049548': [{'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GG'},\n", + " {'variant_id': 'rs2242480', 'gene': 'CYP3A4', 'allele': 'C'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'C'}],\n", + " 'PMC2885890': [{'variant_id': 'rs6473797', 'gene': 'OPRK1', 'allele': 'C'},\n", + " {'variant_id': 'rs1534891', 'gene': 'CSNK1E', 'allele': 'T'},\n", + " {'variant_id': 'rs694066', 'gene': 'GAL', 'allele': 'A'},\n", + " {'variant_id': 'rs2236861', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs3766951', 'gene': 'OPRD1', 'allele': 'C'},\n", + " {'variant_id': 'rs2236857', 'gene': 'OPRD1', 'allele': 'C'},\n", + " {'variant_id': 'rs3758987', 'gene': 'HTR3B', 'allele': 'C'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs3778151', 'gene': 'OPRM1', 'allele': 'C'},\n", + " {'variant_id': 'rs510769', 'gene': 'OPRM1', 'allele': 'T'}],\n", + " 'PMC3647228': [{'variant_id': 'rs11045585',\n", + " 'gene': 'SLCO1B3',\n", + " 'allele': 'AA'},\n", + " {'variant_id': 'rs12762549', 'gene': 'ABCC2', 'allele': 'CC'},\n", + " {'variant_id': 'rs11045585', 'gene': 'SLCO1B3', 'allele': 'AA'},\n", + " {'variant_id': 'rs12762549', 'gene': 'ABCC2', 'allele': 'CG + GG'}],\n", + " 'PMC3081884': [{'variant_id': 'rs16969968', 'gene': 'CHRNA5', 'allele': 'G'},\n", + " {'variant_id': 'rs1051730', 'gene': 'CHRNA3', 'allele': 'G'},\n", + " {'variant_id': 'rs16969968', 'gene': 'CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs1051730', 'gene': 'CHRNA3', 'allele': 'A'}],\n", + " 'PMC9578229': [{'variant_id': 'CYP2C8 poor metabolizer genotype',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2C9 poor metabolizer genotype',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': None}],\n", + " 'PMC5941712': [{'variant_id': 'rs1500', 'gene': 'CRHBP', 'allele': 'G'},\n", + " {'variant_id': 'rs10482672', 'gene': 'NR3C1', 'allele': 'A'},\n", + " {'variant_id': 'rs4234955', 'gene': 'NPY1R, NPY5R', 'allele': 'G'}],\n", + " 'PMC1721229': [{'variant_id': 'CYP2D6*1, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*41'}],\n", + " 'PMC10058912': [{'variant_id': 'CYP2D6 poor metabolizer genotype',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC4308646': [{'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 normal metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC1884377': [{'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2 + *2/*3'}],\n", + " 'PMC3162652': [{'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC4522406': [{'variant_id': 'CYP2C9*1, CYP2C9*2, CYP2C9*3, CYP2C9*5, CYP2C9*6, CYP2C9*8, CYP2C9*9, CYP2C9*11',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'CYP2C8*1, CYP2C8*2, CYP2C8*3, CYP2C8*5, CYP2C8*6, CYP2C8*8, CYP2C8*9, CYP2C8*11',\n", + " 'gene': 'CYP2C8',\n", + " 'allele': '*1/*1'}],\n", + " 'PMC2991048': [{'variant_id': 'CYP2D6 normal metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 normal metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC4554698': [{'variant_id': 'rs35694136', 'gene': 'CYP1A2', 'allele': 'T'},\n", + " {'variant_id': 'rs762551', 'gene': 'CYP1A2', 'allele': 'A'},\n", + " {'variant_id': 'rs5751876', 'gene': 'ADORA2A', 'allele': 'C'},\n", + " {'variant_id': 'rs3032740', 'gene': 'ADORA2A', 'allele': 'TTTTT'}],\n", + " 'PMC3394993': [{'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC3594083': [{'variant_id': 'CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*2'},\n", + " {'variant_id': 'CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2/*2'}],\n", + " 'PMC3843022': [{'variant_id': 'rs1065852', 'gene': 'CYP2D6', 'allele': 'AA'}],\n", + " 'PMC4534836': [{'variant_id': 'rs112445441', 'gene': 'KRAS', 'allele': 'T'}],\n", + " 'PMC3080612': [{'variant_id': 'rs762551', 'gene': 'CYP1A2', 'allele': 'A'},\n", + " {'variant_id': 'rs6968865', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2472297', 'gene': 'CYP1A1', 'allele': 'T'}],\n", + " 'PMC3418550': [{'variant_id': 'rs761142', 'gene': 'GCLC', 'allele': 'AA'},\n", + " {'variant_id': 'rs761142', 'gene': 'GCLC', 'allele': 'A'},\n", + " {'variant_id': 'rs761142', 'gene': 'GCLC', 'allele': 'CC'}],\n", + " 'PMC2014324': [{'variant_id': 'CYP2C19 normal metabolizers',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC10946077': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*6 + *28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6'}],\n", + " 'PMC10951231': [{'variant_id': 'CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10/*10'},\n", + " {'variant_id': 'CYP2D6*10', 'gene': 'CYP2D6', 'allele': '*10/*10'},\n", + " {'variant_id': 'CYP2D6*10', 'gene': 'CYP2D6', 'allele': '*10/*10'}],\n", + " 'PMC10968582': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4 + *4/*4'}],\n", + " 'PMC3590652': [{'variant_id': 'rs2276707', 'gene': 'NR1I2', 'allele': 'TT'},\n", + " {'variant_id': 'rs2981582', 'gene': 'FGFR2', 'allele': 'AA'},\n", + " {'variant_id': 'rs2307424', 'gene': 'NR1I3', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs307826', 'gene': 'FLT4', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs4073054', 'gene': 'NR1I3', 'allele': 'AA'},\n", + " {'variant_id': 'rs307821', 'gene': 'FLT4', 'allele': 'AA + AC'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AG + GG'}],\n", + " 'PMC10995391': [{'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': None},\n", + " {'variant_id': 'NAT2 slow acetylator', 'gene': 'NAT2', 'allele': None},\n", + " {'variant_id': 'NAT2 slow acetylator', 'gene': 'NAT2', 'allele': None},\n", + " {'variant_id': 'rs6413432', 'gene': 'CYP2E1', 'allele': 'A'},\n", + " {'variant_id': 'NAT2 slow acetylator', 'gene': 'NAT2', 'allele': None}],\n", + " 'PMC11021981': [{'variant_id': 'HLA-DRB1*04:03',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*04:03'},\n", + " {'variant_id': 'HLA-DRB1*14:54', 'gene': 'HLA-DRB1', 'allele': '*14:54'},\n", + " {'variant_id': 'HLA-DRB1*04:03', 'gene': 'HLA-DRB1', 'allele': '*04:03'},\n", + " {'variant_id': 'rs62242178', 'gene': 'LIMD1', 'allele': 'C'},\n", + " {'variant_id': 'rs62242177', 'gene': 'LIMD1', 'allele': 'G'}],\n", + " 'PMC11040848': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'},\n", + " {'variant_id': 'rs9263745', 'gene': 'CCHCR1', 'allele': 'A'},\n", + " {'variant_id': 'rs3099844', 'gene': 'CYCSP5', 'allele': 'A'},\n", + " {'variant_id': 'rs9263733', 'gene': 'PSORS1C2', 'allele': 'T'},\n", + " {'variant_id': 'rs9263726', 'gene': 'PSORS1C1, PSORS1C2', 'allele': 'A'}],\n", + " 'PMC11050003': [{'variant_id': 'SLCO1B1*15',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*15'},\n", + " {'variant_id': 'SLCO1B1*20', 'gene': 'SLCO1B1', 'allele': '*20'},\n", + " {'variant_id': 'SLCO1B1*37', 'gene': 'SLCO1B1', 'allele': '*37'},\n", + " {'variant_id': 'SLCO1B1*5', 'gene': 'SLCO1B1', 'allele': '*5'},\n", + " {'variant_id': 'SLCO1B1*15', 'gene': 'SLCO1B1', 'allele': '*15'},\n", + " {'variant_id': 'SLCO1B1*5', 'gene': 'SLCO1B1', 'allele': '*5'},\n", + " {'variant_id': 'SLCO1B1*15', 'gene': 'SLCO1B1', 'allele': '*15'},\n", + " {'variant_id': 'SLCO1B1*14', 'gene': 'SLCO1B1', 'allele': '*14'},\n", + " {'variant_id': 'rs374113543', 'gene': 'SLCO1B1', 'allele': 'A'},\n", + " {'variant_id': 'rs373327528', 'gene': 'SLCO1B1', 'allele': 'A'}],\n", + " 'PMC11162739': [{'variant_id': 'CYP3A5*1, CYP3A5*3, CYP3A5*6',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1'}],\n", + " 'PMC3128690': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC11062152': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*6 + *28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*6/*6 + *28/*28 + *6/*28'}],\n", + " 'PMC11179831': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3 + *2/*2 + *2/*3 + *3/*3'}],\n", + " 'PMC3207930': [{'variant_id': 'rs602633',\n", + " 'gene': 'CELSR2, PSRC1',\n", + " 'allele': 'T'},\n", + " {'variant_id': 'rs445925', 'gene': 'APOC1, APOE', 'allele': 'A'},\n", + " {'variant_id': 'rs174541',\n", + " 'gene': 'FADS1, FEN1, MIR611, TMEM258',\n", + " 'allele': 'T'},\n", + " {'variant_id': 'rs258494', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs6511720', 'gene': 'LDLR', 'allele': 'T'}],\n", + " 'PMC2749467': [{'variant_id': 'rs2230345', 'gene': 'GRK5', 'allele': 'AA'},\n", + " {'variant_id': 'rs1801253', 'gene': 'ADRB1', 'allele': 'CC'},\n", + " {'variant_id': 'rs2230345', 'gene': 'GRK5', 'allele': 'AA'},\n", + " {'variant_id': 'rs1801253', 'gene': 'ADRB1', 'allele': 'CC'},\n", + " {'variant_id': 'rs2230345', 'gene': 'GRK5', 'allele': 'AA'}],\n", + " 'PMC11124388': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'}],\n", + " 'PMC3394137': [{'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs28399499', 'gene': 'CYP2B6', 'allele': 'C'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'A'}],\n", + " 'PMC2745641': [{'variant_id': 'rs1110976', 'gene': 'DRD2', 'allele': 'GG'},\n", + " {'variant_id': 'rs5751876', 'gene': 'ADORA2A', 'allele': 'TT'},\n", + " {'variant_id': 'rs2298383', 'gene': 'ADORA2A', 'allele': 'CC'},\n", + " {'variant_id': 'rs4822492', 'gene': None, 'allele': 'CC'}],\n", + " 'PMC2518503': [{'variant_id': 'rs1111875', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs10811661', 'gene': None, 'allele': 'C'}],\n", + " 'PMC4006820': [{'variant_id': 'rs9332377', 'gene': 'COMT', 'allele': 'T'},\n", + " {'variant_id': 'rs1051640', 'gene': 'ABCC3', 'allele': 'G'},\n", + " {'variant_id': 'rs4646316', 'gene': 'COMT', 'allele': 'C'},\n", + " {'variant_id': 'rs12201199', 'gene': 'TPMT', 'allele': 'T'},\n", + " {'variant_id': 'rs1800460', 'gene': 'TPMT', 'allele': 'T'},\n", + " {'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'C'}],\n", + " 'PMC9484078': [{'variant_id': 'rs316003',\n", + " 'gene': 'SLC22A2',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1448784', 'gene': 'ABCG2', 'allele': 'GG'}],\n", + " 'PMC3880259': [{'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs868853', 'gene': 'ABCC4', 'allele': 'C'},\n", + " {'variant_id': 'rs2274407', 'gene': 'ABCC4', 'allele': 'A'},\n", + " {'variant_id': 'rs9516519', 'gene': 'ABCC4', 'allele': 'G'},\n", + " {'variant_id': 'rs3740065', 'gene': 'ABCC2', 'allele': 'G'},\n", + " {'variant_id': 'rs2231137', 'gene': 'ABCG2', 'allele': 'C'},\n", + " {'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'T'}],\n", + " 'PMC3529742': [{'variant_id': 'rs17137967',\n", + " 'gene': None,\n", + " 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs12795437', 'gene': 'KCNJ1', 'allele': 'C'},\n", + " {'variant_id': 'rs11600347', 'gene': 'KCNJ1', 'allele': 'A'},\n", + " {'variant_id': 'rs658903', 'gene': 'KCNJ1', 'allele': 'A'},\n", + " {'variant_id': 'rs675388', 'gene': 'KCNJ1', 'allele': 'A'},\n", + " {'variant_id': 'rs2846680', 'gene': 'KCNJ1', 'allele': 'A'},\n", + " {'variant_id': 'rs59172778', 'gene': 'KCNJ1', 'allele': 'AG'},\n", + " {'variant_id': 'rs59172778', 'gene': 'KCNJ1', 'allele': 'G'}],\n", + " 'PMC4943390': [{'variant_id': 'CYP2D6 normal metabolizer and ultrarapid metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizers and intermediate metabolizers',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC3055324': [{'variant_id': 'rs2072660', 'gene': 'CHRNB2', 'allele': 'T'},\n", + " {'variant_id': 'rs2072661', 'gene': 'CHRNB2', 'allele': 'A'}],\n", + " 'PMC8822703': [{'variant_id': 'rs3735451', 'gene': 'CYP3A4', 'allele': 'CC'},\n", + " {'variant_id': 'rs10882526', 'gene': 'CYP2C8', 'allele': 'GG'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'TT'}],\n", + " 'PMC2291379': [{'variant_id': 'CYP2D6*1, CYP2D6*4',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'rs3892097', 'gene': 'CYP2D6', 'allele': 'TT'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*4', 'gene': 'CYP2D6', 'allele': '*4/*4'},\n", + " {'variant_id': 'rs3892097', 'gene': 'CYP2D6', 'allele': 'TT'}],\n", + " 'PMC5352797': [{'variant_id': 'rs1131882', 'gene': 'TBXA2R', 'allele': 'A'}],\n", + " 'PMC4259283': [{'variant_id': 'rs11280056',\n", + " 'gene': 'TYMS',\n", + " 'allele': 'TTAAAGTTA/TTAAAGTTA'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'GG'}],\n", + " 'PMC2855513': [{'variant_id': 'rs12806698', 'gene': 'RRM1', 'allele': 'AC'},\n", + " {'variant_id': 'rs9937', 'gene': 'RRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1042858', 'gene': 'RRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1042858', 'gene': 'RRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs12806698', 'gene': 'RRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs9937', 'gene': 'RRM1', 'allele': 'GG'}],\n", + " 'PMC11204263': [{'variant_id': 'rs3892097',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': 'CT + TT'}],\n", + " 'PMC2039873': [{'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'TT'},\n", + " {'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'CT'},\n", + " {'variant_id': 'rs1800497', 'gene': 'ANKK1', 'allele': 'GG'}],\n", + " 'PMC2819190': [{'variant_id': 'rs11628713', 'gene': 'PAPLN', 'allele': 'T'}],\n", + " 'PMC2262891': [{'variant_id': 'rs13181',\n", + " 'gene': 'ERCC2',\n", + " 'allele': 'GG + GT'}],\n", + " 'PMC10993165': [{'variant_id': 'HLA-B*15:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-B*13:01', 'gene': 'HLA-B', 'allele': '*13:01'},\n", + " {'variant_id': 'HLA-B*38:02', 'gene': 'HLA-B', 'allele': '*38:02'}],\n", + " 'PMC9501033': [{'variant_id': 'rs3842', 'gene': 'ABCB1', 'allele': 'CC'},\n", + " {'variant_id': 'rs13306198', 'gene': 'APOB', 'allele': 'AA + AG'}],\n", + " 'PMC11143062': [{'variant_id': 'CYP2C19 rapid metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC11154978': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3 + *2/*2 + *2/*3 + *3/*3'}],\n", + " 'PMC4171106': [{'variant_id': 'rs784888', 'gene': 'SP1', 'allele': 'G'},\n", + " {'variant_id': 'rs784892', 'gene': 'AMHR2', 'allele': 'G'},\n", + " {'variant_id': 'rs784892', 'gene': 'AMHR2', 'allele': 'G'},\n", + " {'variant_id': 'rs2683511', 'gene': 'SP1', 'allele': 'C'},\n", + " {'variant_id': 'rs10747673', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs10747673', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs784888', 'gene': 'SP1', 'allele': 'G'},\n", + " {'variant_id': 'rs149711321', 'gene': 'PPARA', 'allele': 'T'},\n", + " {'variant_id': 'rs2683511', 'gene': 'SP1', 'allele': 'C'}],\n", + " 'PMC11255757': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*9, CYP2C19*13, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *2/*17 + *1/*9 + *2/*13 + *9/*17 + *2/*2'}],\n", + " 'PMC6946839': [{'variant_id': 'CYP2C19 intermediate metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC5485718': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'},\n", + " {'variant_id': 'rs12456693', 'gene': 'SLC14A2', 'allele': 'CT'},\n", + " {'variant_id': 'rs2487032', 'gene': 'ABCA1', 'allele': 'A'},\n", + " {'variant_id': 'rs2254638', 'gene': 'N6AMT1', 'allele': 'G'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2', 'gene': 'CYP2C19', 'allele': '*2'},\n", + " {'variant_id': 'rs2254638', 'gene': 'N6AMT1', 'allele': 'AG + GG'}],\n", + " 'PMC11232816': [{'variant_id': 'CYP2C9*1, CYP2C9*2',\n", + " 'gene': 'CYP2C9',\n", + " 'allele': '*2'}],\n", + " 'PMC11421297': [{'variant_id': 'HLA-A*26:01',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*26:01'}],\n", + " 'PMC11203737': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*1 + *1/*3'}],\n", + " 'PMC10344568': [{'variant_id': 'rs12979860',\n", + " 'gene': 'IFNL3, IFNL4',\n", + " 'allele': 'TT'}],\n", + " 'PMC11204531': [{'variant_id': 'HLA-DPB1*05:01',\n", + " 'gene': 'HLA-DPB1',\n", + " 'allele': '*05:01'},\n", + " {'variant_id': 'HLA-A*11:01', 'gene': 'HLA-A', 'allele': '*11:01'}],\n", + " 'PMC11201799': [{'variant_id': 'NAT2*5, NAT2*6, NAT2*30',\n", + " 'gene': 'NAT2',\n", + " 'allele': '*5 + *6 + *30'},\n", + " {'variant_id': 'rs1799930', 'gene': 'NAT2', 'allele': 'A'},\n", + " {'variant_id': 'rs1801280', 'gene': 'NAT2', 'allele': 'C'}],\n", + " 'PMC11218103': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28'}],\n", + " 'PMC3349771': [{'variant_id': 'rs892940', 'gene': 'THRB', 'allele': 'A'}],\n", + " 'PMC2949659': [{'variant_id': 'CYP2D6*1, CYP2D6*4, CYP2D6*5, CYP2D6*9, CYP2D6*10, CYP2D6*41',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*4 + *5 + *9 + *10 + *41'}],\n", + " 'PMC4345853': [{'variant_id': 'CYP2D6*1, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*10'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2306283', 'gene': 'SLCO1B1', 'allele': 'AG + GG'}],\n", + " 'PMC11245871': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC3691683': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC11282083': [{'variant_id': 'HLA-B*55:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*55:02'},\n", + " {'variant_id': 'HLA-C*01:02', 'gene': 'HLA-C', 'allele': '*01:02'},\n", + " {'variant_id': 'HLA-DQB1*06:09', 'gene': 'HLA-DQB1', 'allele': '*06:09'}],\n", + " 'PMC11451421': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3 + *2/*2 + *2/*3 + *3/*3'}],\n", + " 'PMC11165025': [{'variant_id': 'HLA-B*15:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:01'}],\n", + " 'PMC8707980': [{'variant_id': 'rs200643089', 'gene': 'DPYD', 'allele': 'AC'},\n", + " {'variant_id': 'rs76387818', 'gene': 'DPYD', 'allele': 'AG'},\n", + " {'variant_id': 'rs367619008', 'gene': 'DPYD', 'allele': 'C'},\n", + " {'variant_id': 'rs944174134', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs2786783', 'gene': 'DPYD', 'allele': 'A'}],\n", + " 'PMC11246114': [{'variant_id': 'rs5219',\n", + " 'gene': 'ABCC8, KCNJ11',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2289669', 'gene': 'SLC47A1', 'allele': 'A'},\n", + " {'variant_id': 'rs72552763', 'gene': 'SLC22A1', 'allele': 'GAT/del'},\n", + " {'variant_id': 'rs628031', 'gene': 'SLC22A1', 'allele': 'AA + AG'}],\n", + " 'PMC11269678': [{'variant_id': 'HLA-DQA1*05',\n", + " 'gene': 'HLA-DQA1',\n", + " 'allele': '*05'},\n", + " {'variant_id': 'rs2097432', 'gene': None, 'allele': 'CC + CT'},\n", + " {'variant_id': 'HLA-DQA1*05', 'gene': 'HLA-DQA1', 'allele': '*05'}],\n", + " 'PMC11286099': [{'variant_id': 'NUDT15*1, NUDT15*3',\n", + " 'gene': 'NUDT15',\n", + " 'allele': '*1/*3'},\n", + " {'variant_id': 'TPMT*1, TPMT*3C', 'gene': 'TPMT', 'allele': '*1/*3C'}],\n", + " 'PMC11480601': [{'variant_id': 'CYP3A5*1, CYP3A5*3, CYP3A5*6',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*3 + *1/*6 + *3/*3 + *3/*6'}],\n", + " 'PMC11288125': [{'variant_id': 'HLA-B*57:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*57:01'}],\n", + " 'PMC11480591': [{'variant_id': 'GSTT1 non-null, GSTT1 null',\n", + " 'gene': 'GSTT1',\n", + " 'allele': None},\n", + " {'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': None},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'AG + GG'}],\n", + " 'PMC11324585': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2'}],\n", + " 'PMC11328459': [{'variant_id': 'CYP2C19 poor metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None}],\n", + " 'PMC7653527': [{'variant_id': 'UGT1A1*1, UGT1A1*6',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*6 + *6/*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*1/*28'}],\n", + " 'PMC11331244': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'}],\n", + " 'PMC3516223': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28 + *1/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6/*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6/*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28 + *1/*28'}],\n", + " 'PMC11159193': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *1/*6'}],\n", + " 'PMC3117027': [{'variant_id': 'UGT1A1*1, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28'}],\n", + " 'PMC11492647': [{'variant_id': 'UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28 + *6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*28', 'gene': 'UGT1A1', 'allele': '*28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6', 'gene': 'UGT1A1', 'allele': '*6/*6'}],\n", + " 'PMC11359404': [{'variant_id': 'CYP2D6 intermediate metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'NAT2 slow acetylator', 'gene': 'NAT2', 'allele': None},\n", + " {'variant_id': 'rs2011425', 'gene': 'UGT1A4', 'allele': 'GT'},\n", + " {'variant_id': 'rs2008584', 'gene': 'UGT1A3', 'allele': 'AA'},\n", + " {'variant_id': 'rs12708954', 'gene': 'SLC6A2', 'allele': 'CC'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'TT'},\n", + " {'variant_id': 'CYP2D6 intermediate metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'SLCO1B1 deficiency', 'gene': 'SLCO1B1', 'allele': None}],\n", + " 'PMC11354576': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG'},\n", + " {'variant_id': 'CYP2D6 ultrarapid metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC11359302': [{'variant_id': 'CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*3/*3'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GT'},\n", + " {'variant_id': 'rs2622604', 'gene': 'ABCG2', 'allele': 'CT'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'GG'}],\n", + " 'PMC4915569': [{'variant_id': 'CYP2C19*1, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*17'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*8',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3 + *8'},\n", + " {'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3, CYP2C19*8',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2 + *3 + *8'},\n", + " {'variant_id': 'rs71647871', 'gene': 'CES1', 'allele': 'T'}],\n", + " 'PMC10661737': [{'variant_id': 'CYP2D6*1, CYP2D6*1x2, CYP2D6*2, CYP2D6*2x2, CYP2D6*2xN, CYP2D6*4, CYP2D6*9, CYP2D6*10, CYP2D6*41, CYP2D6*41x2',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*1x2 + *1/*2x2 + *1x2/*41 + *2/*2x2 + *2xN/*4 + *1/*1 + *1/*10 + *1/*2 + *1/*41 + *1/*41x2 + *1/*9 + *2/*2 + *2/*41 + *2/*9'}],\n", + " 'PMC11345242': [{'variant_id': 'HLA-B*15:02',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02'}],\n", + " 'PMC5488790': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*6 + *28'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*6 + *28'}],\n", + " 'PMC5480170': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28 + *6/*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*28 + *28/*28 + *6/*6'},\n", + " {'variant_id': 'DPYD c.1627A>G (*5)',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'c.1627A>G (*5)'}],\n", + " 'PMC1442447': [{'variant_id': 'NAT2 slow acetylator',\n", + " 'gene': 'NAT2',\n", + " 'allele': None}],\n", + " 'PMC7995603': [{'variant_id': 'CYP3A4 poor metabolizer',\n", + " 'gene': 'CYP3A4',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP3A5 poor metabolizer', 'gene': 'CYP3A5', 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizer', 'gene': 'CYP2D6', 'allele': None},\n", + " {'variant_id': 'rs1414334', 'gene': 'HTR2C', 'allele': 'G'},\n", + " {'variant_id': 'rs6280', 'gene': 'DRD3', 'allele': 'T'},\n", + " {'variant_id': 'rs1799978', 'gene': 'DRD2', 'allele': 'C'},\n", + " {'variant_id': 'rs6277', 'gene': 'DRD2', 'allele': 'A'},\n", + " {'variant_id': 'rs6311', 'gene': 'HTR2A', 'allele': 'TT'}],\n", + " 'PMC3151555': [{'variant_id': 'rs6280', 'gene': 'DRD3', 'allele': 'CC + CT'}],\n", + " 'PMC4291053': [{'variant_id': 'CYP2A6 low activity',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': None}],\n", + " 'PMC11538333': [{'variant_id': 'rs2268979', 'gene': 'ACTN1', 'allele': 'T'},\n", + " {'variant_id': 'rs12235805', 'gene': 'PAPPA', 'allele': 'A'},\n", + " {'variant_id': 'rs12253008', 'gene': 'NRG3', 'allele': 'T'},\n", + " {'variant_id': 'rs10997459', 'gene': 'LRRTM3', 'allele': 'G'},\n", + " {'variant_id': 'rs8006511', 'gene': 'ARHGAP5', 'allele': 'G'},\n", + " {'variant_id': 'rs10961381', 'gene': 'NFIB', 'allele': 'G'},\n", + " {'variant_id': 'rs1815857', 'gene': 'MCM3AP', 'allele': 'T'},\n", + " {'variant_id': 'rs12474420', 'gene': 'SPDYA', 'allele': 'T'},\n", + " {'variant_id': 'rs79802223', 'gene': 'METTL8', 'allele': 'G'},\n", + " {'variant_id': 'rs12658429', 'gene': 'PDE4D', 'allele': 'C'},\n", + " {'variant_id': 'rs12656510', 'gene': 'FBN2', 'allele': 'T'},\n", + " {'variant_id': 'rs17129858', 'gene': 'VTI1A', 'allele': 'T'},\n", + " {'variant_id': 'rs200858088', 'gene': 'ZFAND3', 'allele': 'C'}],\n", + " 'PMC11430620': [{'variant_id': 'DPYD c.2194G>A (*6)',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'c.2194G>A (*6)'}],\n", + " 'PMC6006041': [{'variant_id': 'rs1801265',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC11441158': [{'variant_id': 'DPYD c.1129-5923C>G, c.1236G>A (HapB3), DPYD c.1679T>G (*13), DPYD c.2846A>T',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'c.2846A>T + c.1129-5923C>G, c.1236G>A (HapB3) + c.1679T>G (*13)'}],\n", + " 'PMC11456491': [{'variant_id': 'DPYD c.2846A>T, DPYD c.2872A>G',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'c.2872A>G + c.2846A>T'}],\n", + " 'PMC3491093': [{'variant_id': 'rs1045642',\n", + " 'gene': 'ABCB1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA'}],\n", + " 'PMC7417535': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28 + *1/*28 + *1/*6 + *6/*28'}],\n", + " 'PMC7137309': [{'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*1'}],\n", + " 'PMC8586680': [{'variant_id': 'UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*6'},\n", + " {'variant_id': 'UGT1A1*1, UGT1A1*6, UGT1A1*28',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*28/*28 + *6/*6 + *6/*28'}],\n", + " 'PMC3579268': [{'variant_id': 'CYP2D6 poor metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC1029622': [{'variant_id': 'TPMT deficiency',\n", + " 'gene': 'TPMT',\n", + " 'allele': None},\n", + " {'variant_id': 'TPMT deficiency', 'gene': 'TPMT', 'allele': None}],\n", + " 'PMC4446052': [{'variant_id': 'TPMT intermediate metabolizer',\n", + " 'gene': 'TPMT',\n", + " 'allele': None},\n", + " {'variant_id': 'TPMT intermediate metabolizer',\n", + " 'gene': 'TPMT',\n", + " 'allele': None}],\n", + " 'PMC3898327': [{'variant_id': 'TPMT normal metabolizer',\n", + " 'gene': 'TPMT',\n", + " 'allele': None}],\n", + " 'PMC11695165': [{'variant_id': 'NAT2 rapid acetylator',\n", + " 'gene': 'NAT2',\n", + " 'allele': None},\n", + " {'variant_id': 'rs1799929', 'gene': 'NAT2', 'allele': 'T'}],\n", + " 'PMC11519535': [{'variant_id': 'rs4148325', 'gene': 'UGT1A1', 'allele': 'CT'},\n", + " {'variant_id': 'rs11888492', 'gene': 'UGT1A1', 'allele': 'G'}],\n", + " 'PMC11671340': [{'variant_id': 'SLCO1B1*9, SLCO1B1*37',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*9/*37'}],\n", + " 'PMC5813097': [{'variant_id': 'CYP2C19*1, CYP2C19*2',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2'}],\n", + " 'PMC11578071': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC11703518': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'},\n", + " {'variant_id': 'HLA-A*02:06', 'gene': 'HLA-A', 'allele': '*02:06'},\n", + " {'variant_id': 'HLA-C*08:01', 'gene': 'HLA-C', 'allele': '*08:01'}],\n", + " 'PMC11603346': [{'variant_id': 'CYP2B6*1, CYP2B6*6',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*1/*6 + *6/*6'},\n", + " {'variant_id': 'CYP2B6*1, CYP2B6*6',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*1/*6 + *6/*6'}],\n", + " 'PMC11950614': [{'variant_id': 'HLA-B*58:01',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*58:01'}],\n", + " 'PMC11178516': [{'variant_id': 'CYP2C19*1, CYP2C19*2, CYP2C19*3',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*1/*2 + *1/*3 + *2/*2 + *2/*3 + *3/*3'}],\n", + " 'PMC11622454': [{'variant_id': 'NAT2*5, NAT2*6, NAT2*7',\n", + " 'gene': 'NAT2',\n", + " 'allele': '*5/*7 + *5/*6 + *6/*6'}],\n", + " 'PMC11703413': [{'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*4, CYP2D6*4xN, CYP2D6*5, CYP2D6*9, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4 + *1/*5 + *2/*4 + *2/*4xN + *4/*4'},\n", + " {'variant_id': 'CYP2D6*1, CYP2D6*2, CYP2D6*4, CYP2D6*4xN, CYP2D6*5, CYP2D6*9, CYP2D6*10',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*1/*4 + *1/*5 + *2/*4 + *2/*4xN + *4/*4'}],\n", + " 'PMC11606684': [{'variant_id': 'GSTM1 non-null, GSTM1 null',\n", + " 'gene': 'GSTM1',\n", + " 'allele': None},\n", + " {'variant_id': 'GSTT1 non-null, GSTT1 null',\n", + " 'gene': 'GSTT1',\n", + " 'allele': None}],\n", + " 'PMC4533232': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*3'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'CYP3A5*1, CYP3A5*3', 'gene': 'CYP3A5', 'allele': '*1/*3'}],\n", + " 'PMC7215842': [{'variant_id': 'CYP2D6 intermediate metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC10858860': [{'variant_id': 'rs1799971',\n", + " 'gene': 'OPRM1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AA + AG'}],\n", + " 'PMC11628428': [{'variant_id': 'DPYD c.85T>C (*9A)',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'c.85T>C (*9A)/c.85T>C (*9A)'},\n", + " {'variant_id': 'DPYD c.1627A>G (*5)',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'c.1627A>G (*5)/c.1627A>G (*5)'}],\n", + " 'PMC11634619': [{'variant_id': 'SLCO1B1*1, SLCO1B1*15, SLCO1B1*37',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': '*37/*37 + *37/*15'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AG + GG'}],\n", + " 'PMC11661530': [{'variant_id': 'UGT1A1*1, UGT1A1*36',\n", + " 'gene': 'UGT1A1',\n", + " 'allele': '*1/*36'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs1801160', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC2950822': [{'variant_id': 'rs12042763', 'gene': 'PTGS2', 'allele': 'GG'}],\n", + " 'PMC12008353': [{'variant_id': 'rs1052576', 'gene': 'CASP9', 'allele': 'T'},\n", + " {'variant_id': 'rs4661636', 'gene': 'CASP9', 'allele': 'C'},\n", + " {'variant_id': 'rs6736233', 'gene': 'CASP8', 'allele': 'G'},\n", + " {'variant_id': 'rs1800630', 'gene': 'TNF', 'allele': 'C'}],\n", + " 'PMC10537526': [{'variant_id': 'CYP2D6 poor metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 poor metabolizer', 'gene': 'CYP2D6', 'allele': None}],\n", + " 'PMC5427156': [{'variant_id': 'CYP2C19*2, CYP2C19*17',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': '*2/*17'}],\n", + " 'PMC10607223': [{'variant_id': 'rs34059508',\n", + " 'gene': 'SLC22A1',\n", + " 'allele': 'AG'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs34059508', 'gene': 'SLC22A1', 'allele': 'AG'}],\n", + " 'PMC11732454': [{'variant_id': 'HLA-A*24:02',\n", + " 'gene': 'HLA-A',\n", + " 'allele': '*24:02'},\n", + " {'variant_id': 'HLA-B*15:02', 'gene': 'HLA-B', 'allele': '*15:02'},\n", + " {'variant_id': 'HLA-A*01:02', 'gene': 'HLA-A', 'allele': '*01:02'},\n", + " {'variant_id': 'HLA-B*52:01', 'gene': 'HLA-B', 'allele': '*52:01'},\n", + " {'variant_id': 'HLA-DRB1*03:01', 'gene': 'HLA-DRB1', 'allele': '*03:01'},\n", + " {'variant_id': 'HLA-DRB1*13:01', 'gene': 'HLA-DRB1', 'allele': '*13:01'},\n", + " {'variant_id': 'HLA-A*02:01', 'gene': 'HLA-A', 'allele': '*02:01'},\n", + " {'variant_id': 'HLA-B*40:02', 'gene': 'HLA-B', 'allele': '*40:02'},\n", + " {'variant_id': 'HLA-A*31:03', 'gene': 'HLA-A', 'allele': '*31:03'}],\n", + " 'PMC10778960': [{'variant_id': 'rs2612091',\n", + " 'gene': 'ENOSF1',\n", + " 'allele': 'TT'}],\n", + " 'PMC2955782': [{'variant_id': 'rs2228075', 'gene': 'IMPDH1', 'allele': 'T'}],\n", + " 'PMC11638798': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC11745869': [{'variant_id': 'rs10764319',\n", + " 'gene': 'CACNB2',\n", + " 'allele': 'CT'},\n", + " {'variant_id': 'rs2236957', 'gene': 'CACNA2D2', 'allele': 'AA'},\n", + " {'variant_id': 'rs1841042', 'gene': 'UGT2B10', 'allele': 'AA'},\n", + " {'variant_id': 'rs3813662', 'gene': 'ADRA2B', 'allele': 'AA + CC'},\n", + " {'variant_id': 'rs8192733', 'gene': 'CYP2A6', 'allele': 'GG'},\n", + " {'variant_id': 'rs3814057', 'gene': 'NR1I2', 'allele': 'CC'}],\n", + " 'PMC3673930': [{'variant_id': 'CYP2D6*2',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': '*2/*2'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'}],\n", + " 'PMC3250350': [{'variant_id': 'rs854560', 'gene': 'PON1', 'allele': 'A'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'A'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'C'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'C'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'C'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'C'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'A'}],\n", + " 'PMC11770582': [{'variant_id': 'HLA-B*15:02, HLA-B*15:12, HLA-B*15:27',\n", + " 'gene': 'HLA-B',\n", + " 'allele': '*15:02 + *15:12 + *15:27'}],\n", + " 'PMC11993291': [{'variant_id': 'rs7807369', 'gene': 'THSD7A', 'allele': 'G'},\n", + " {'variant_id': 'rs1149515', 'gene': None, 'allele': 'C'}],\n", + " 'PMC11802319': [{'variant_id': 'CYP2D6 poor metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC11789932': [{'variant_id': 'CYP2D6 normal metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2D6 ultrarapid metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC11800520': [{'variant_id': 'rs17524572',\n", + " 'gene': 'ERAP2',\n", + " 'allele': 'TT'}],\n", + " 'PMC11786012': [{'variant_id': 'HLA-DRB1*13:01',\n", + " 'gene': 'HLA-DRB1',\n", + " 'allele': '*13:01'},\n", + " {'variant_id': 'HLA-DPB1*02:02', 'gene': 'HLA-DPB1', 'allele': '*02:02'}],\n", + " 'PMC11923007': [{'variant_id': 'CYP2D6 normal metabolizer',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': None}],\n", + " 'PMC5591462': [{'variant_id': 'rs72552763',\n", + " 'gene': 'SLC22A1',\n", + " 'allele': 'del/del'},\n", + " {'variant_id': 'rs12208357', 'gene': 'SLC22A1', 'allele': 'T'}],\n", + " 'PMC4110463': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC2490838': [{'variant_id': 'G6PD deficiency',\n", + " 'gene': 'G6PD',\n", + " 'allele': None}],\n", + " 'PMC11573588': [{'variant_id': 'rs148954387',\n", + " 'gene': 'SPINK1',\n", + " 'allele': 'AG'}],\n", + " 'PMC11905633': [{'variant_id': 'CYP3A5*1, CYP3A5*3',\n", + " 'gene': 'CYP3A5',\n", + " 'allele': '*1/*3'}],\n", + " 'PMC12087686': [{'variant_id': 'rs77271279',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': 'T'},\n", + " {'variant_id': 'rs59502379', 'gene': 'SLCO1B1', 'allele': 'C'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'C'}],\n", + " 'PMC3055343': [{'variant_id': 'rs1049353', 'gene': 'CNR1', 'allele': 'CC'},\n", + " {'variant_id': 'rs806378', 'gene': 'CNR1', 'allele': 'CT + TT'}],\n", + " 'PMC3071630': [{'variant_id': 'rs4410790', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2470893', 'gene': 'CYP1A1', 'allele': 'T'}],\n", + " 'PMC2151292': [{'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs1801159', 'gene': 'DPYD', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs1801158', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC3833422': [{'variant_id': 'rs11887534', 'gene': 'ABCG8', 'allele': 'CG'},\n", + " {'variant_id': 'rs11887534', 'gene': 'ABCG8', 'allele': 'CG'}],\n", + " 'PMC3003450': [{'variant_id': 'rs3813929', 'gene': 'HTR2C', 'allele': 'T'},\n", + " {'variant_id': 'rs1414334', 'gene': 'HTR2C', 'allele': 'C'}],\n", + " 'PMC4237616': [{'variant_id': 'rs10878232',\n", + " 'gene': 'WIF1',\n", + " 'allele': 'GG + GT'},\n", + " {'variant_id': 'rs12819505', 'gene': 'WNT5B', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4541111', 'gene': 'AXIN2', 'allele': 'AA + AC'},\n", + " {'variant_id': 'rs4413407', 'gene': 'CXXC4', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs11868547', 'gene': 'AXIN2', 'allele': 'CC + CG'}],\n", + " 'PMC6930822': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs495491', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs702764', 'gene': 'OPRK1', 'allele': 'C'},\n", + " {'variant_id': 'rs609148', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs6473797', 'gene': 'OPRK1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs6473797', 'gene': 'OPRK1', 'allele': 'C'},\n", + " {'variant_id': 'rs495491', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs702764', 'gene': 'OPRK1', 'allele': 'C'},\n", + " {'variant_id': 'rs609148', 'gene': 'OPRM1', 'allele': 'A'}],\n", + " 'PMC3454425': [{'variant_id': 'TPMT*1, TPMT*3A',\n", + " 'gene': 'TPMT',\n", + " 'allele': '*1/*1'},\n", + " {'variant_id': 'rs1127354', 'gene': 'ITPA', 'allele': 'CC'}],\n", + " 'PMC5972502': [{'variant_id': 'rs267606619',\n", + " 'gene': 'MT-RNR1',\n", + " 'allele': 'T'}],\n", + " 'PMC5958681': [{'variant_id': 'rs267606617',\n", + " 'gene': 'MT-RNR1',\n", + " 'allele': 'G'}],\n", + " 'PMC3320544': [{'variant_id': 'rs2269879',\n", + " 'gene': 'DOT1L',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2269879', 'gene': 'DOT1L', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs12350051', 'gene': 'MLLT3', 'allele': 'CC + CT'}],\n", + " 'PMC2879583': [{'variant_id': 'rs12364283', 'gene': 'DRD2', 'allele': 'AA'}],\n", + " 'PMC10031538': [{'variant_id': 'rs34394661', 'gene': 'CD80', 'allele': 'AA'}],\n", + " 'PMC4366259': [{'variant_id': 'rs1053023', 'gene': 'STAT3', 'allele': 'C'},\n", + " {'variant_id': 'rs1053005', 'gene': 'STAT3', 'allele': 'C'},\n", + " {'variant_id': 'rs1053004', 'gene': 'STAT3', 'allele': 'G'},\n", + " {'variant_id': 'rs2227956', 'gene': 'HSPA1L', 'allele': 'G'},\n", + " {'variant_id': 'rs1524107', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2066992', 'gene': 'IL6', 'allele': 'T'},\n", + " {'variant_id': 'rs2069837', 'gene': 'IL6', 'allele': 'G'}],\n", + " 'PMC3410976': [{'variant_id': 'rs135543', 'gene': 'PPARA', 'allele': 'TT'},\n", + " {'variant_id': 'rs9626730', 'gene': 'PPARA', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs135550', 'gene': 'PPARA', 'allele': 'TT'}],\n", + " 'PMC7039325': [{'variant_id': 'CYP2C19 intermediate metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'CYP2C19 poor metabolizer',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': None},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs7997012', 'gene': 'HTR2A', 'allele': 'G'},\n", + " {'variant_id': 'rs6313', 'gene': 'HTR2A', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'G'}],\n", + " 'PMC6519044': [{'variant_id': 'rs1013940',\n", + " 'gene': 'SLC5A7',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs10504361', 'gene': 'NKAIN3', 'allele': 'A'},\n", + " {'variant_id': 'rs3784867', 'gene': 'ABCC1', 'allele': 'T'},\n", + " {'variant_id': 'rs924607', 'gene': 'CEP72', 'allele': 'TT'}],\n", + " 'PMC10851571': [{'variant_id': 'rs1229984', 'gene': 'ADH1B', 'allele': 'T'}],\n", + " 'PMC4130802': [{'variant_id': 'rs6947309', 'gene': 'LUC7L2', 'allele': 'T'},\n", + " {'variant_id': 'rs16849146', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs4784333', 'gene': 'FTO', 'allele': 'C'}],\n", + " 'PMC3945213': [{'variant_id': 'rs11603334', 'gene': 'ARAP1', 'allele': 'A'},\n", + " {'variant_id': 'rs340874', 'gene': 'PROX1', 'allele': 'C'}],\n", + " 'PMC3326190': [{'variant_id': 'rs2286455', 'gene': 'PROM1', 'allele': 'CC'},\n", + " {'variant_id': 'rs3130', 'gene': 'PROM1', 'allele': 'CC'},\n", + " {'variant_id': 'rs2286455', 'gene': 'PROM1', 'allele': 'CC'}],\n", + " 'PMC3060561': [{'variant_id': 'rs2357928', 'gene': 'CACNB2', 'allele': 'GG'},\n", + " {'variant_id': 'rs2357928', 'gene': 'CACNB2', 'allele': 'AA'},\n", + " {'variant_id': 'rs2357928', 'gene': 'CACNB2', 'allele': 'AG'},\n", + " {'variant_id': 'rs11014166', 'gene': 'CACNB2', 'allele': 'AA'},\n", + " {'variant_id': 'rs11014166', 'gene': 'CACNB2', 'allele': 'AT + TT'}],\n", + " 'PMC3671611': [{'variant_id': 'rs2252281', 'gene': 'SLC47A1', 'allele': 'CC'},\n", + " {'variant_id': 'rs2252281', 'gene': 'SLC47A1', 'allele': 'CC'},\n", + " {'variant_id': 'rs12943590', 'gene': 'SLC47A2', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs12943590', 'gene': 'SLC47A2', 'allele': 'AA'}],\n", + " 'PMC9562471': [{'variant_id': 'rs4646450', 'gene': 'CYP3A5', 'allele': 'A'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'T'},\n", + " {'variant_id': 'rs6977165', 'gene': 'CYP3A5', 'allele': 'C'}],\n", + " 'PMC5559481': [{'variant_id': 'rs10132552', 'gene': 'MEG3', 'allele': 'C'},\n", + " {'variant_id': 'rs36080650', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs1059698', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs73594404', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs2027701', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs3743773', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs1829346', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs1829346', 'gene': None, 'allele': 'AA'},\n", + " {'variant_id': 'rs2027701', 'gene': None, 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1059698', 'gene': None, 'allele': 'CC'},\n", + " {'variant_id': 'rs73594404', 'gene': None, 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs36080650', 'gene': None, 'allele': 'CC'},\n", + " {'variant_id': 'rs10132552', 'gene': 'MEG3', 'allele': 'CC'}],\n", + " 'PMC3859145': [{'variant_id': 'rs34116584', 'gene': 'AGXT', 'allele': 'T'},\n", + " {'variant_id': 'rs3136228', 'gene': 'MSH6', 'allele': 'GG'},\n", + " {'variant_id': 'rs1799794', 'gene': 'XRCC3', 'allele': 'CC'},\n", + " {'variant_id': 'rs2074087', 'gene': 'ABCC1', 'allele': 'C'},\n", + " {'variant_id': 'rs1885301', 'gene': 'ABCC2', 'allele': 'AA'},\n", + " {'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs1138272', 'gene': 'GSTP1', 'allele': 'T'},\n", + " {'variant_id': 'rs4426527', 'gene': 'AGXT', 'allele': 'G'}],\n", + " 'PMC10772501': [{'variant_id': 'rs11540822', 'gene': 'CCHCR1', 'allele': 'T'},\n", + " {'variant_id': 'rs2874964', 'gene': 'RFXAP', 'allele': 'G'}],\n", + " 'PMC11000398': [{'variant_id': 'rs9344', 'gene': 'CCND1', 'allele': 'A'},\n", + " {'variant_id': 'rs9344', 'gene': 'CCND1', 'allele': 'AA'}],\n", + " 'PMC3055737': [{'variant_id': 'rs2283265', 'gene': 'DRD2', 'allele': 'AA'},\n", + " {'variant_id': 'rs1125394', 'gene': 'DRD2', 'allele': 'C'},\n", + " {'variant_id': 'rs1076560', 'gene': 'DRD2', 'allele': 'A'},\n", + " {'variant_id': 'rs2283265', 'gene': 'DRD2', 'allele': 'A'}],\n", + " 'PMC11555502': [{'variant_id': 'rs4646449', 'gene': 'CYP3A5', 'allele': 'CC'},\n", + " {'variant_id': 'rs4646453', 'gene': 'CYP3A5', 'allele': 'CC'},\n", + " {'variant_id': 'rs15524', 'gene': 'CYP3A5', 'allele': 'AA'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'CC'},\n", + " {'variant_id': 'rs4646453', 'gene': 'CYP3A5', 'allele': 'CC'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'CC'}],\n", + " 'PMC10039478': [{'variant_id': 'rs1799971',\n", + " 'gene': 'OPRM1',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs677830', 'gene': 'OPRM1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2296616', 'gene': 'MIR107', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1011784', 'gene': 'MIR23B', 'allele': 'GG'},\n", + " {'variant_id': 'rs1011784', 'gene': 'MIR23B', 'allele': 'CG + GG'},\n", + " {'variant_id': 'rs677830', 'gene': 'OPRM1', 'allele': 'CT + TT'}],\n", + " 'PMC21386': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1799972', 'gene': 'OPRM1', 'allele': 'T'}],\n", + " 'PMC3377371': [{'variant_id': 'rs510769', 'gene': 'OPRM1', 'allele': 'TT'},\n", + " {'variant_id': 'rs2281617', 'gene': 'OPRM1', 'allele': 'CC'}],\n", + " 'PMC10151137': [{'variant_id': 'rs11265375',\n", + " 'gene': 'NHLH1',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs3806915', 'gene': 'SEMA6A', 'allele': 'A'},\n", + " {'variant_id': 'rs11265375', 'gene': 'NHLH1', 'allele': 'T'},\n", + " {'variant_id': 'rs3806915', 'gene': 'SEMA6A', 'allele': 'A'}],\n", + " 'PMC10189922': [{'variant_id': 'rs915854', 'gene': 'CBS', 'allele': 'TT'},\n", + " {'variant_id': 'rs2839629', 'gene': 'PKNOX1', 'allele': 'AA'}],\n", + " 'PMC3083026': [{'variant_id': 'rs12979860',\n", + " 'gene': 'IFNL3, IFNL4',\n", + " 'allele': 'CC'}],\n", + " 'PMC11277774': [{'variant_id': 'rs4149601', 'gene': 'NEDD4L', 'allele': 'AA'},\n", + " {'variant_id': 'rs1801252', 'gene': 'ADRB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5, ZSCAN25', 'allele': 'CC'}],\n", + " 'PMC4420233': [{'variant_id': 'rs2229109', 'gene': 'ABCB1', 'allele': 'CT'}],\n", + " 'PMC10308004': [{'variant_id': 'rs2000999', 'gene': 'HPR', 'allele': 'GG'},\n", + " {'variant_id': 'rs35283911', 'gene': None, 'allele': 'GG'},\n", + " {'variant_id': 'rs2000999', 'gene': 'HPR', 'allele': 'G'},\n", + " {'variant_id': 'rs35283911', 'gene': None, 'allele': 'G'}],\n", + " 'PMC7270956': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC5694018': [{'variant_id': 'rs3750625', 'gene': 'ADRA2A', 'allele': 'A'},\n", + " {'variant_id': 'rs11195418', 'gene': 'ADRA2A', 'allele': 'G'},\n", + " {'variant_id': 'rs1800038', 'gene': 'ADRA2A', 'allele': 'A'}],\n", + " 'PMC7099631': [{'variant_id': 'rs12422149',\n", + " 'gene': 'SLCO2B1',\n", + " 'allele': 'GG'}],\n", + " 'PMC2963015': [{'variant_id': 'rs3763980', 'gene': 'SLC16A7', 'allele': 'A'}],\n", + " 'PMC4350985': [{'variant_id': 'rs854568', 'gene': 'PON1', 'allele': 'A'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'T'},\n", + " {'variant_id': 'rs13306698', 'gene': 'PON1', 'allele': 'T'},\n", + " {'variant_id': 'rs854572', 'gene': 'PON1', 'allele': 'G'},\n", + " {'variant_id': 'rs854573', 'gene': 'PON1', 'allele': 'T'},\n", + " {'variant_id': 'rs854552', 'gene': 'PON1', 'allele': 'T'},\n", + " {'variant_id': 'rs854565', 'gene': 'PON1', 'allele': 'G'}],\n", + " 'PMC2955237': [{'variant_id': 'rs75017182', 'gene': 'DPYD', 'allele': 'C'},\n", + " {'variant_id': 'rs56038477', 'gene': 'DPYD', 'allele': 'T'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC10319068': [{'variant_id': 'rs7148',\n", + " 'gene': 'TMSB10',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC4165277': [{'variant_id': 'rs11122576',\n", + " 'gene': 'AGT',\n", + " 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs11122576', 'gene': 'AGT', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs11122576', 'gene': 'AGT', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs11122576', 'gene': 'AGT', 'allele': 'CC + CT'}],\n", + " 'PMC9613789': [{'variant_id': 'rs4864950', 'gene': 'KDR', 'allele': 'A'},\n", + " {'variant_id': 'rs4864950', 'gene': 'KDR', 'allele': 'A'},\n", + " {'variant_id': 'rs444904', 'gene': 'PIK3R5', 'allele': 'T'},\n", + " {'variant_id': 'rs4864950', 'gene': 'KDR', 'allele': 'A'},\n", + " {'variant_id': 'rs444904', 'gene': 'PIK3R5', 'allele': 'T'},\n", + " {'variant_id': 'rs2330951', 'gene': 'EGFR', 'allele': 'C'},\n", + " {'variant_id': 'rs11125039', 'gene': 'PRKCE', 'allele': 'G'},\n", + " {'variant_id': 'rs1346563', 'gene': 'ADAMTS18', 'allele': 'A'},\n", + " {'variant_id': 'rs1868089', 'gene': 'EPAS1', 'allele': 'C'},\n", + " {'variant_id': 'rs12366035', 'gene': 'VEGFB', 'allele': 'T'},\n", + " {'variant_id': 'rs12366035', 'gene': 'VEGFB', 'allele': 'T'},\n", + " {'variant_id': 'rs9927200', 'gene': 'WWOX', 'allele': 'A'},\n", + " {'variant_id': 'rs11651488', 'gene': 'MAP2K6', 'allele': 'C'},\n", + " {'variant_id': 'rs17682789', 'gene': 'CDH13', 'allele': 'C'},\n", + " {'variant_id': 'rs917881', 'gene': 'EGFR', 'allele': 'A'},\n", + " {'variant_id': 'rs917881', 'gene': 'EGFR', 'allele': 'A'},\n", + " {'variant_id': 'rs9973653', 'gene': 'EPAS1', 'allele': 'T'},\n", + " {'variant_id': 'rs315498', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs4035887', 'gene': 'EPAS1', 'allele': 'A'},\n", + " {'variant_id': 'rs4035887', 'gene': 'EPAS1', 'allele': 'A'},\n", + " {'variant_id': 'rs10958704', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs7557402', 'gene': 'EPAS1', 'allele': 'G'},\n", + " {'variant_id': 'rs12948059', 'gene': 'MAP2K6', 'allele': 'G'}],\n", + " 'PMC4282597': [{'variant_id': 'rs6465084', 'gene': 'GRM3', 'allele': 'AA'},\n", + " {'variant_id': 'rs1468412', 'gene': 'GRM3', 'allele': 'TT'}],\n", + " 'PMC3619396': [{'variant_id': 'rs45607939', 'gene': 'NAT2', 'allele': 'T'},\n", + " {'variant_id': 'rs1799931', 'gene': 'NAT2', 'allele': 'A'},\n", + " {'variant_id': 'rs1208', 'gene': 'NAT2', 'allele': 'G'},\n", + " {'variant_id': 'rs75160992', 'gene': 'CYB5A', 'allele': 'C'},\n", + " {'variant_id': 'rs77005399', 'gene': 'CYB5A', 'allele': 'T'},\n", + " {'variant_id': 'rs1801280', 'gene': 'NAT2', 'allele': 'C'},\n", + " {'variant_id': 'rs1041983', 'gene': 'NAT2', 'allele': 'T'},\n", + " {'variant_id': 'rs1799930', 'gene': 'NAT2', 'allele': 'A'},\n", + " {'variant_id': 'rs1799929', 'gene': 'NAT2', 'allele': 'T'},\n", + " {'variant_id': 'rs76458556', 'gene': 'CYB5R3', 'allele': 'T'},\n", + " {'variant_id': 'rs7663179', 'gene': 'CYB5A', 'allele': 'T'},\n", + " {'variant_id': 'rs8190370', 'gene': 'CYB5R3', 'allele': 'A'},\n", + " {'variant_id': 'rs7284807', 'gene': 'CYB5R3', 'allele': 'G'},\n", + " {'variant_id': 'rs77499608', 'gene': 'CYB5R3', 'allele': 'G'}],\n", + " 'PMC4566931': [{'variant_id': 'rs1870377', 'gene': 'KDR', 'allele': 'AA'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'GG'},\n", + " {'variant_id': 'rs4073', 'gene': 'CXCL8', 'allele': 'AA'}],\n", + " 'PMC6800829': [{'variant_id': 'rs1042597', 'gene': 'UGT1A8', 'allele': 'G'},\n", + " {'variant_id': 'rs2741049', 'gene': 'UGT1A9', 'allele': 'T'},\n", + " {'variant_id': 'rs2306283', 'gene': 'SLCO1B1', 'allele': 'G'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'T'},\n", + " {'variant_id': 'rs4149117', 'gene': 'SLCO1B3', 'allele': 'T'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'G'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'G'},\n", + " {'variant_id': 'rs4149117', 'gene': 'SLCO1B3', 'allele': 'T'},\n", + " {'variant_id': 'rs4149117', 'gene': 'SLCO1B3', 'allele': 'T'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'G'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4149117', 'gene': 'SLCO1B3', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs7311358', 'gene': 'SLCO1B3', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4149117', 'gene': 'SLCO1B3', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs4149117', 'gene': 'SLCO1B3', 'allele': 'GT + TT'}],\n", + " 'PMC7202258': [{'variant_id': 'rs2934965', 'gene': 'PNMT', 'allele': 'T'},\n", + " {'variant_id': 'rs2941523', 'gene': 'PNMT', 'allele': 'GG'},\n", + " {'variant_id': 'rs876493', 'gene': 'PNMT', 'allele': 'AA + AG'}],\n", + " 'PMC5754426': [{'variant_id': 'rs2229109', 'gene': 'ABCB1', 'allele': 'CT'},\n", + " {'variant_id': 'rs2229109', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs2229109', 'gene': 'ABCB1', 'allele': 'CT'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'}],\n", + " 'PMC2343589': [{'variant_id': 'rs2305799',\n", + " 'gene': 'ACP5',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2229531', 'gene': 'ACP5', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2228570', 'gene': 'VDR', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs6092', 'gene': 'SERPINE1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs6254', 'gene': 'PTH', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1800247', 'gene': 'BGLAP', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2234693', 'gene': 'ESR1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs2306862', 'gene': 'LRP5', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1138518', 'gene': 'PTH1R', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA + AG'}],\n", + " 'PMC4519298': [{'variant_id': 'rs885036', 'gene': 'MGAT4A', 'allele': 'G'},\n", + " {'variant_id': 'rs885036', 'gene': 'MGAT4A', 'allele': 'G'},\n", + " {'variant_id': 'rs2073016', 'gene': 'APOBEC2', 'allele': 'T'},\n", + " {'variant_id': 'rs2978931', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs4377367', 'gene': 'ARHGEF4', 'allele': 'T'},\n", + " {'variant_id': 'rs2928607', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs11997869', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs2928609', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2928608', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2978926', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs2912024', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2936519', 'gene': None, 'allele': 'G'}],\n", + " 'PMC6889277': [{'variant_id': 'rs2366929', 'gene': 'ADGRV1', 'allele': 'C'}],\n", + " 'PMC3604077': [{'variant_id': 'rs429358',\n", + " 'gene': 'APOE',\n", + " 'allele': 'CC + CT'}],\n", + " 'PMC11011248': [{'variant_id': 'rs932658', 'gene': 'SIRT1', 'allele': 'A'}],\n", + " 'PMC11558073': [{'variant_id': 'rs3892097',\n", + " 'gene': 'CYP2D6',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'AC'},\n", + " {'variant_id': 'rs1137101', 'gene': 'LEPR', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4880', 'gene': 'SOD2', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1137101', 'gene': 'LEPR', 'allele': 'AG + GG'}],\n", + " 'PMC5411211': [{'variant_id': 'rs6785930',\n", + " 'gene': 'P2RY12',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs6809699', 'gene': 'P2RY12', 'allele': 'AA + AC'},\n", + " {'variant_id': 'rs6785930', 'gene': 'P2RY12', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs6809699', 'gene': 'P2RY12', 'allele': 'AA + AC'}],\n", + " 'PMC2945219': [{'variant_id': 'rs3788853', 'gene': 'XPNPEP2', 'allele': 'A'}],\n", + " 'PMC2664534': [{'variant_id': 'rs7992226', 'gene': 'NALCN', 'allele': 'A'},\n", + " {'variant_id': 'rs9345389', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs4888024', 'gene': None, 'allele': 'G'}],\n", + " 'PMC3909071': [{'variant_id': 'rs274618', 'gene': 'GRM3', 'allele': 'T'},\n", + " {'variant_id': 'rs274622', 'gene': 'GRM3', 'allele': 'C'},\n", + " {'variant_id': 'rs1990040', 'gene': 'GRM3', 'allele': 'A'},\n", + " {'variant_id': 'rs13242038', 'gene': 'GRM3', 'allele': 'T'},\n", + " {'variant_id': 'rs724226', 'gene': 'GRM3', 'allele': 'A'},\n", + " {'variant_id': 'rs2189814', 'gene': 'GRM3', 'allele': 'T'},\n", + " {'variant_id': 'rs917071', 'gene': 'GRM3', 'allele': 'T'},\n", + " {'variant_id': 'rs6465088', 'gene': 'GRM3', 'allele': 'G'},\n", + " {'variant_id': 'rs2299225', 'gene': 'GRM3', 'allele': 'G'},\n", + " {'variant_id': 'rs1468412', 'gene': 'GRM3', 'allele': 'T'},\n", + " {'variant_id': 'rs2237562', 'gene': 'GRM3', 'allele': 'C'},\n", + " {'variant_id': 'rs6947784', 'gene': 'GRM3', 'allele': 'C'},\n", + " {'variant_id': 'rs2237558', 'gene': 'GRM3', 'allele': 'A'},\n", + " {'variant_id': 'rs2282966', 'gene': 'GRM3', 'allele': 'A'},\n", + " {'variant_id': 'rs1989796', 'gene': 'GRM3', 'allele': 'C'},\n", + " {'variant_id': 'rs17160170', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs7789655', 'gene': 'GRM3', 'allele': 'G'}],\n", + " 'PMC1275610': [{'variant_id': 'rs3788853', 'gene': 'XPNPEP2', 'allele': 'A'}],\n", + " 'PMC6168379': [{'variant_id': 'rs3734704', 'gene': 'HSP90AB1', 'allele': 'C'},\n", + " {'variant_id': 'rs6929249', 'gene': 'HSP90AB1', 'allele': 'G'},\n", + " {'variant_id': 'rs9381299', 'gene': 'PRKCA', 'allele': 'C'},\n", + " {'variant_id': 'rs834576', 'gene': 'HSP90AB1', 'allele': 'A'}],\n", + " 'PMC2875867': [{'variant_id': 'rs28399499', 'gene': 'CYP2B6', 'allele': 'CT'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'TT'},\n", + " {'variant_id': 'rs28365062', 'gene': 'UGT2B7', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs28399454', 'gene': 'CYP2A6', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs8192726', 'gene': 'CYP2A6', 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs7439366', 'gene': 'UGT2B7', 'allele': 'CC + CT'}],\n", + " 'PMC8175348': [{'variant_id': 'rs116982346', 'gene': 'TRAT1', 'allele': 'C'},\n", + " {'variant_id': 'rs11753309', 'gene': 'HLA-B', 'allele': 'A'},\n", + " {'variant_id': 'rs377360', 'gene': 'TRAC', 'allele': 'A'},\n", + " {'variant_id': 'rs73482673', 'gene': 'ELAVL2', 'allele': 'A'}],\n", + " 'PMC2917014': [{'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'CC'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'CT'}],\n", + " 'PMC3245349': [{'variant_id': 'rs1789891', 'gene': 'ADH1B', 'allele': 'A'}],\n", + " 'PMC5263176': [{'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'T'}],\n", + " 'PMC2902654': [{'variant_id': 'rs2245964', 'gene': 'FGFBP1', 'allele': 'CC'},\n", + " {'variant_id': 'rs16892645', 'gene': 'FGFBP1', 'allele': 'TT'}],\n", + " 'PMC4048019': [{'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'TT'}],\n", + " 'PMC3966292': [{'variant_id': 'rs2824292', 'gene': None, 'allele': 'G'}],\n", + " 'PMC3248052': [{'variant_id': 'rs10912675', 'gene': 'FMO1', 'allele': 'C'},\n", + " {'variant_id': 'rs7877', 'gene': 'FMO1', 'allele': 'T'}],\n", + " 'PMC4852013': [{'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'T'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'T'}],\n", + " 'PMC3000799': [{'variant_id': 'rs4958381', 'gene': 'GRIA1', 'allele': 'T'}],\n", + " 'PMC11382849': [{'variant_id': 'rs743572',\n", + " 'gene': 'CYP17A1',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'AA + AG'}],\n", + " 'PMC4547828': [{'variant_id': 'rs12457042',\n", + " 'gene': 'TNFRSF11A',\n", + " 'allele': 'T'},\n", + " {'variant_id': 'rs2073618', 'gene': 'TNFRSF11B', 'allele': 'CC'},\n", + " {'variant_id': 'rs7237982', 'gene': 'TNFRSF11A', 'allele': 'G'},\n", + " {'variant_id': 'rs4941129', 'gene': 'TNFRSF11A', 'allele': 'T'},\n", + " {'variant_id': 'rs2073618', 'gene': 'TNFRSF11B', 'allele': 'CC'},\n", + " {'variant_id': 'rs7984870', 'gene': 'TNFSF11', 'allele': 'CC'},\n", + " {'variant_id': 'rs7984870', 'gene': 'TNFSF11', 'allele': 'CC'},\n", + " {'variant_id': 'rs11573856', 'gene': 'TNFRSF11B', 'allele': 'T'},\n", + " {'variant_id': 'rs3102728', 'gene': 'TNFRSF11B', 'allele': 'C'},\n", + " {'variant_id': 'rs3134068', 'gene': 'TNFRSF11B', 'allele': 'C'},\n", + " {'variant_id': 'rs1485288', 'gene': 'TNFRSF11B', 'allele': 'C'},\n", + " {'variant_id': 'rs313458', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs6567270', 'gene': 'TNFRSF11A', 'allele': 'A'},\n", + " {'variant_id': 'rs12455775', 'gene': 'TNFRSF11A', 'allele': 'G'},\n", + " {'variant_id': 'rs11877530', 'gene': 'TNFRSF11A', 'allele': 'A'},\n", + " {'variant_id': 'rs11664594', 'gene': 'TNFRSF11A', 'allele': 'T'},\n", + " {'variant_id': 'rs4369774', 'gene': 'TNFRSF11A', 'allele': 'C'},\n", + " {'variant_id': 'rs12956925', 'gene': 'TNFRSF11A', 'allele': 'A'},\n", + " {'variant_id': 'rs12969154', 'gene': 'TNFRSF11A', 'allele': 'A'},\n", + " {'variant_id': 'rs7239621', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs1485286', 'gene': 'TNFRSF11B', 'allele': 'T'},\n", + " {'variant_id': 'rs3102724', 'gene': 'TNFRSF11B', 'allele': 'A'},\n", + " {'variant_id': 'rs2277438', 'gene': 'TNFSF11', 'allele': 'A'},\n", + " {'variant_id': 'rs9566990', 'gene': 'TNFSF11', 'allele': 'G'},\n", + " {'variant_id': 'rs9533166', 'gene': 'TNFSF11', 'allele': 'C'},\n", + " {'variant_id': 'rs9594782', 'gene': 'TNFSF11', 'allele': 'C'},\n", + " {'variant_id': 'rs6567276', 'gene': 'TNFRSF11A', 'allele': 'C'},\n", + " {'variant_id': 'rs9646629', 'gene': 'TNFRSF11A', 'allele': 'G'},\n", + " {'variant_id': 'rs430363', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs4941131', 'gene': 'TNFRSF11A', 'allele': 'C'},\n", + " {'variant_id': 'rs7239667', 'gene': 'TNFRSF11A', 'allele': 'C'}],\n", + " 'PMC3798385': [{'variant_id': 'rs139887', 'gene': 'SOX10', 'allele': 'CG'},\n", + " {'variant_id': 'rs544093', 'gene': 'OPRM1', 'allele': 'GG'},\n", + " {'variant_id': 'rs2849380', 'gene': 'BCL2', 'allele': 'TT'},\n", + " {'variant_id': 'rs544093', 'gene': 'OPRM1', 'allele': 'TT'},\n", + " {'variant_id': 'rs879207', 'gene': None, 'allele': 'AG'},\n", + " {'variant_id': 'rs139887', 'gene': 'SOX10', 'allele': 'CC'},\n", + " {'variant_id': 'rs2849380', 'gene': 'BCL2', 'allele': 'CC'},\n", + " {'variant_id': 'rs879207', 'gene': None, 'allele': 'AA'}],\n", + " 'PMC3353487': [{'variant_id': 'rs2306283', 'gene': 'SLCO1B1', 'allele': 'G'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'C'}],\n", + " 'PMC3074105': [{'variant_id': 'rs5442', 'gene': 'GNB3, USP5', 'allele': 'AA'},\n", + " {'variant_id': 'rs5442', 'gene': 'GNB3, USP5', 'allele': 'AA'},\n", + " {'variant_id': 'rs5443', 'gene': 'GNB3', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs5443', 'gene': 'GNB3', 'allele': 'CT + TT'}],\n", + " 'PMC7423195': [{'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GG'},\n", + " {'variant_id': 'rs2622604', 'gene': 'ABCG2', 'allele': 'TT'},\n", + " {'variant_id': 'rs2622604', 'gene': 'ABCG2', 'allele': 'TT'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GG'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GG'},\n", + " {'variant_id': 'rs2622604', 'gene': 'ABCG2', 'allele': 'TT'},\n", + " {'variant_id': 'rs2231142', 'gene': 'ABCG2', 'allele': 'GG'},\n", + " {'variant_id': 'rs2622604', 'gene': 'ABCG2', 'allele': 'TT'}],\n", + " 'PMC10139129': [{'variant_id': 'rs2249015', 'gene': 'ENPP2', 'allele': 'AA'},\n", + " {'variant_id': 'rs7832704', 'gene': 'ENPP2', 'allele': 'AA'},\n", + " {'variant_id': 'rs2249015', 'gene': 'ENPP2', 'allele': 'AA'},\n", + " {'variant_id': 'rs7832704', 'gene': 'ENPP2', 'allele': 'A'}],\n", + " 'PMC2679106': [{'variant_id': 'rs8192726',\n", + " 'gene': 'CYP2A6',\n", + " 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'TT'},\n", + " {'variant_id': 'rs28399454', 'gene': 'CYP2A6', 'allele': 'TT'},\n", + " {'variant_id': 'rs28399499', 'gene': 'CYP2B6', 'allele': 'CT'}],\n", + " 'PMC8042025': [{'variant_id': 'rs113332494',\n", + " 'gene': 'HLA-DQB1',\n", + " 'allele': 'G'},\n", + " {'variant_id': 'rs113332494', 'gene': 'HLA-DQB1', 'allele': 'G'}],\n", + " 'PMC3960598': [{'variant_id': 'rs4680', 'gene': None, 'allele': 'GG'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'AG'}],\n", + " 'PMC5812637': [{'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'AA'}],\n", + " 'PMC6777606': [{'variant_id': 'rs679899', 'gene': 'APOB', 'allele': 'G'},\n", + " {'variant_id': 'rs1367117', 'gene': 'APOB', 'allele': 'G'},\n", + " {'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'A'},\n", + " {'variant_id': 'rs429358', 'gene': 'APOC1, APOE', 'allele': 'C'},\n", + " {'variant_id': 'rs7412', 'gene': 'APOC1, APOE', 'allele': 'T'},\n", + " {'variant_id': 'rs693', 'gene': 'APOB', 'allele': 'A'},\n", + " {'variant_id': 'rs405509', 'gene': 'APOE', 'allele': 'G'},\n", + " {'variant_id': 'rs769450', 'gene': 'APOE', 'allele': 'A'},\n", + " {'variant_id': 'rs439401', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'C'},\n", + " {'variant_id': 'rs13306194', 'gene': 'APOB', 'allele': 'A'},\n", + " {'variant_id': 'rs1042034', 'gene': 'APOB', 'allele': 'T'}],\n", + " 'PMC5846189': [{'variant_id': 'rs712829', 'gene': 'EGFR', 'allele': 'GG'},\n", + " {'variant_id': 'rs2622604', 'gene': 'ABCG2', 'allele': 'CC'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'C'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'C'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'TT'}],\n", + " 'PMC5534361': [{'variant_id': 'rs1803274', 'gene': 'BCHE', 'allele': 'T'}],\n", + " 'PMC4982803': [{'variant_id': 'rs1061781',\n", + " 'gene': 'B4GALT2',\n", + " 'allele': 'CT + TT'}],\n", + " 'PMC3831180': [{'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'C'},\n", + " {'variant_id': 'rs4693075', 'gene': 'COQ2', 'allele': 'C'}],\n", + " 'PMC5157199': [{'variant_id': 'rs113488022',\n", + " 'gene': 'BRAF',\n", + " 'allele': 'AT + TT'}],\n", + " 'PMC2686874': [{'variant_id': 'rs28933396', 'gene': 'RYR1', 'allele': 'A'}],\n", + " 'PMC2587308': [{'variant_id': 'rs4713916', 'gene': 'FKBP5', 'allele': 'A'}],\n", + " 'PMC3514624': [{'variant_id': 'rs1611115', 'gene': 'DBH', 'allele': 'CC'}],\n", + " 'PMC3505921': [{'variant_id': 'rs1051266',\n", + " 'gene': 'SLC19A1',\n", + " 'allele': 'CC + TT'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'TT'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'AA'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'GG'},\n", + " {'variant_id': 'rs1051266', 'gene': 'SLC19A1', 'allele': 'CC'}],\n", + " 'PMC7390973': [{'variant_id': 'rs12979860',\n", + " 'gene': 'IFNL3, IFNL4',\n", + " 'allele': 'CT + TT'}],\n", + " 'PMC4339275': [{'variant_id': 'rs1799853', 'gene': 'CYP2C9', 'allele': 'TT'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'AC'}],\n", + " 'PMC3383117': [{'variant_id': 'rs1056892', 'gene': 'CBR3', 'allele': 'GG'},\n", + " {'variant_id': 'rs9024', 'gene': 'CBR1', 'allele': 'GG'}],\n", + " 'PMC3912740': [{'variant_id': 'rs9922316', 'gene': 'PRKCB', 'allele': 'T'}],\n", + " 'PMC7266722': [{'variant_id': 'rs2395029', 'gene': 'HCP5', 'allele': 'T'},\n", + " {'variant_id': 'rs1229984', 'gene': 'ADH1B', 'allele': 'T'},\n", + " {'variant_id': 'rs698', 'gene': 'ADH1C', 'allele': 'C'},\n", + " {'variant_id': 'rs1800750', 'gene': 'TNF', 'allele': 'AG'},\n", + " {'variant_id': 'rs2227956', 'gene': 'HSPA1A, HSPA1L, LSM2', 'allele': 'A'},\n", + " {'variant_id': 'rs361525', 'gene': 'TNF', 'allele': 'A'}],\n", + " 'PMC6202211': [{'variant_id': 'rs10455872', 'gene': 'LPA', 'allele': 'G'}],\n", + " 'PMC4077354': [{'variant_id': 'rs118192172', 'gene': 'RYR1', 'allele': 'T'}],\n", + " 'PMC2920359': [{'variant_id': 'rs1799732', 'gene': 'DRD2', 'allele': 'del'}],\n", + " 'PMC10520058': [{'variant_id': 'rs4149056',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2306283', 'gene': 'SLCO1B1', 'allele': 'GG'},\n", + " {'variant_id': 'rs3842', 'gene': 'ABCB1', 'allele': 'CC'},\n", + " {'variant_id': 'rs999278', 'gene': 'SLCO1B1', 'allele': 'AA + AC'},\n", + " {'variant_id': 'rs4149057', 'gene': 'SLCO1B1', 'allele': 'CC + CT'}],\n", + " 'PMC2876136': [{'variant_id': 'rs5030868',\n", + " 'gene': 'G6PD, IKBKG',\n", + " 'allele': 'A'}],\n", + " 'PMC10625179': [{'variant_id': 'rs6766410', 'gene': 'HTR3C', 'allele': 'CC'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'TT'},\n", + " {'variant_id': 'rs7943062', 'gene': 'HTR3B', 'allele': 'GG'}],\n", + " 'PMC7409770': [{'variant_id': 'rs62103056', 'gene': None, 'allele': 'A'}],\n", + " 'PMC3787837': [{'variant_id': 'rs2484516', 'gene': 'ADRA2A', 'allele': 'G'}],\n", + " 'PMC5945500': [{'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'G'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'T'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'G'}],\n", + " 'PMC5669583': [{'variant_id': 'rs34550074',\n", + " 'gene': 'SLCO2A1',\n", + " 'allele': 'T'}],\n", + " 'PMC3549321': [{'variant_id': 'rs1695',\n", + " 'gene': 'GSTP1',\n", + " 'allele': 'AG + GG'}],\n", + " 'PMC5300764': [{'variant_id': 'rs11676382', 'gene': 'GGCX', 'allele': 'G'},\n", + " {'variant_id': 'rs699664', 'gene': 'GGCX', 'allele': 'C'},\n", + " {'variant_id': 'rs11676382', 'gene': 'GGCX', 'allele': 'G'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'CT + TT'}],\n", + " 'PMC9031832': [{'variant_id': 'rs1800497', 'gene': 'DRD2', 'allele': 'A'},\n", + " {'variant_id': 'rs1076560', 'gene': 'DRD2', 'allele': 'A'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'A'}],\n", + " 'PMC1820604': [{'variant_id': 'rs1050828', 'gene': 'G6PD', 'allele': 'T'},\n", + " {'variant_id': 'rs1050828', 'gene': 'G6PD', 'allele': 'CT + TT'}],\n", + " 'PMC10702074': [{'variant_id': 'rs6025', 'gene': 'F5', 'allele': 'C'},\n", + " {'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'G'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'G'},\n", + " {'variant_id': 'rs2227631', 'gene': 'SERPINE1', 'allele': 'A'}],\n", + " 'PMC8973308': [{'variant_id': 'rs116855232',\n", + " 'gene': 'NUDT15',\n", + " 'allele': 'CT'},\n", + " {'variant_id': 'rs1800460', 'gene': 'TPMT', 'allele': 'CT'},\n", + " {'variant_id': 'rs1800462', 'gene': 'TPMT', 'allele': 'CG'}],\n", + " 'PMC4624261': [{'variant_id': 'rs7950311', 'gene': 'TRIM5', 'allele': 'C'},\n", + " {'variant_id': 'rs6674079', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs2549714', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs4910232', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs3795247', 'gene': 'ZNF100', 'allele': 'C'}],\n", + " 'PMC4859410': [{'variant_id': 'rs4149056',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': 'CC'}],\n", + " 'PMC2831569': [{'variant_id': 'rs3856806', 'gene': 'PPARG', 'allele': 'TT'},\n", + " {'variant_id': 'rs3856806', 'gene': 'PPARG', 'allele': 'TT'}],\n", + " 'PMC4990373': [{'variant_id': 'rs1934951', 'gene': 'CYP2C8', 'allele': 'CC'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'CC'},\n", + " {'variant_id': 'rs13181', 'gene': 'ERCC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs3212935', 'gene': 'ERCC1', 'allele': 'TT'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1042522', 'gene': 'TP53', 'allele': 'CC'},\n", + " {'variant_id': 'rs1136201', 'gene': 'ERBB2', 'allele': 'AA'},\n", + " {'variant_id': 'rs1042522', 'gene': 'TP53', 'allele': 'CC'},\n", + " {'variant_id': 'rs351855', 'gene': 'FGFR4', 'allele': 'AA'},\n", + " {'variant_id': 'rs351855', 'gene': 'FGFR4', 'allele': 'AA'},\n", + " {'variant_id': 'rs3212935', 'gene': 'ERCC1', 'allele': 'TT'},\n", + " {'variant_id': 'rs13181', 'gene': 'ERCC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs1136201', 'gene': 'ERBB2', 'allele': 'AA'},\n", + " {'variant_id': 'rs2740574', 'gene': 'CYP3A4', 'allele': 'CC'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1934951', 'gene': 'CYP2C8', 'allele': 'CC'}],\n", + " 'PMC11318817': [{'variant_id': 'rs1045642',\n", + " 'gene': 'ABCB1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA + AG'}],\n", + " 'PMC8602039': [{'variant_id': 'rs74569896', 'gene': 'NECAB1', 'allele': 'G'},\n", + " {'variant_id': 'rs4736529', 'gene': 'EFR3A', 'allele': 'G'},\n", + " {'variant_id': 'rs11640115', 'gene': 'WDR24', 'allele': 'G'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'T'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'A'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'A'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'C'},\n", + " {'variant_id': 'rs4986893', 'gene': 'CYP2C19', 'allele': 'A'},\n", + " {'variant_id': 'rs201441480', 'gene': 'KRTAP10-4, TSPEAR', 'allele': 'A'},\n", + " {'variant_id': 'rs140410716', 'gene': 'ECHS1', 'allele': 'T'},\n", + " {'variant_id': 'rs75750968', 'gene': 'AGAP3', 'allele': 'A'},\n", + " {'variant_id': 'rs3749187', 'gene': 'ZDHHC3', 'allele': 'A'},\n", + " {'variant_id': 'rs17064642', 'gene': 'MYOM2', 'allele': 'CC + CT'}],\n", + " 'PMC6714673': [{'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'AC'},\n", + " {'variant_id': 'rs1799853', 'gene': 'CYP2C9', 'allele': 'CT'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'},\n", + " {'variant_id': 'rs1043550', 'gene': 'CALU', 'allele': 'G'},\n", + " {'variant_id': 'rs12714145', 'gene': 'GGCX', 'allele': 'T'},\n", + " {'variant_id': 'rs1051740', 'gene': 'EPHX1', 'allele': 'C'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'T'},\n", + " {'variant_id': 'rs6046', 'gene': 'F7', 'allele': 'A'},\n", + " {'variant_id': 'rs11676382', 'gene': 'GGCX', 'allele': 'G'},\n", + " {'variant_id': 'rs699664', 'gene': 'GGCX', 'allele': 'T'},\n", + " {'variant_id': 'rs1799853', 'gene': 'CYP2C9', 'allele': 'T'},\n", + " {'variant_id': 'rs1057910', 'gene': 'CYP2C9', 'allele': 'C'},\n", + " {'variant_id': 'rs11676382', 'gene': 'GGCX', 'allele': 'G'},\n", + " {'variant_id': 'rs6046', 'gene': 'F7', 'allele': 'A'},\n", + " {'variant_id': 'rs2108622', 'gene': 'CYP4F2', 'allele': 'T'},\n", + " {'variant_id': 'rs699664', 'gene': 'GGCX', 'allele': 'T'},\n", + " {'variant_id': 'rs1043550', 'gene': 'CALU', 'allele': 'G'},\n", + " {'variant_id': 'rs12714145', 'gene': 'GGCX', 'allele': 'T'},\n", + " {'variant_id': 'rs1051740', 'gene': 'EPHX1', 'allele': 'C'},\n", + " {'variant_id': 'rs9923231', 'gene': 'VKORC1', 'allele': 'T'}],\n", + " 'PMC11509751': [{'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'TT'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4148738', 'gene': 'ABCB1', 'allele': 'T'},\n", + " {'variant_id': 'rs4148738', 'gene': 'ABCB1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'AA + AC'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs776746', 'gene': 'CYP3A5', 'allele': 'CC'}],\n", + " 'PMC4110714': [{'variant_id': 'rs10455872',\n", + " 'gene': 'LPA',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs10455872', 'gene': 'LPA', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs10455872', 'gene': 'LPA', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs10455872', 'gene': 'LPA', 'allele': 'AG + GG'}],\n", + " 'PMC2364178': [{'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC4633171': [{'variant_id': 'rs717620',\n", + " 'gene': 'ABCC2',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'C'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'T'}],\n", + " 'PMC10526923': [{'variant_id': 'rs2238476', 'gene': 'ABCC1', 'allele': 'AG'},\n", + " {'variant_id': 'rs3761548', 'gene': 'FOXP3', 'allele': 'GT'},\n", + " {'variant_id': 'rs13120400', 'gene': 'ABCG2', 'allele': 'CT + TT'}],\n", + " 'PMC2988112': [{'variant_id': 'rs2762939', 'gene': 'CYP24A1', 'allele': 'C'}],\n", + " 'PMC2933242': [{'variant_id': 'rs135561',\n", + " 'gene': 'PPARA',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC549606': [{'variant_id': 'rs121434569', 'gene': 'EGFR', 'allele': 'T'}],\n", + " 'PMC3620714': [{'variant_id': 'rs35599367',\n", + " 'gene': 'CYP3A4',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC9529953': [{'variant_id': 'rs74418677', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs912571', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs79048288', 'gene': 'CCDC148', 'allele': 'T'},\n", + " {'variant_id': 'rs1324052', 'gene': None, 'allele': 'A'}],\n", + " 'PMC4898266': [{'variant_id': 'rs2291075',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': 'CT + TT'}],\n", + " 'PMC3380161': [{'variant_id': 'rs1126742',\n", + " 'gene': 'CYP4A11',\n", + " 'allele': 'AG + GG'}],\n", + " 'PMC3841971': [{'variant_id': 'rs2307441', 'gene': 'POLG', 'allele': 'C'},\n", + " {'variant_id': 'rs3087374', 'gene': 'POLG', 'allele': 'A'}],\n", + " 'PMC3248744': [{'variant_id': 'rs35599367',\n", + " 'gene': 'CYP3A4',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC5817388': [{'variant_id': 'rs648743', 'gene': 'CTH', 'allele': 'TT'},\n", + " {'variant_id': 'rs1021737', 'gene': 'CTH', 'allele': 'TT'},\n", + " {'variant_id': 'rs1021737', 'gene': 'CTH', 'allele': 'TT'},\n", + " {'variant_id': 'rs648743', 'gene': 'CTH', 'allele': 'TT'}],\n", + " 'PMC4999819': [{'variant_id': 'rs5888', 'gene': 'SCARB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs10846744', 'gene': 'SCARB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs3782287', 'gene': 'SCARB1', 'allele': 'GG'},\n", + " {'variant_id': 'rs8099917', 'gene': 'IFNL3, IFNL4', 'allele': 'TT'}],\n", + " 'PMC7446391': [{'variant_id': 'rs755416212', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC1734304': [{'variant_id': 'rs28933397', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs118192122', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs121918592', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs193922772', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs118192172', 'gene': 'RYR1', 'allele': 'T'}],\n", + " 'PMC3987224': [{'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'T'}],\n", + " 'PMC9028965': [{'variant_id': 'rs10515807', 'gene': 'ADRA1B', 'allele': 'G'},\n", + " {'variant_id': 'rs553668', 'gene': 'ADRA2A', 'allele': 'G'},\n", + " {'variant_id': 'rs521674', 'gene': 'ADRA2A', 'allele': 'T'},\n", + " {'variant_id': 'rs10515807', 'gene': 'ADRA1B', 'allele': 'A'}],\n", + " 'PMC5137276': [{'variant_id': 'rs1867283', 'gene': 'NTRK2', 'allele': 'A'},\n", + " {'variant_id': 'rs1187286', 'gene': 'NTRK2', 'allele': 'G'},\n", + " {'variant_id': 'rs962369', 'gene': 'BDNF', 'allele': 'C'},\n", + " {'variant_id': 'rs1387923', 'gene': 'NTRK2', 'allele': 'A'},\n", + " {'variant_id': 'rs11140800', 'gene': 'NTRK2', 'allele': 'C'},\n", + " {'variant_id': 'rs10868235', 'gene': 'NTRK2', 'allele': 'C'},\n", + " {'variant_id': 'rs1360550', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs6265', 'gene': 'BDNF', 'allele': 'C'},\n", + " {'variant_id': 'rs2072446', 'gene': 'NGFR', 'allele': 'C'},\n", + " {'variant_id': 'rs2551919', 'gene': 'CREB1', 'allele': 'C'},\n", + " {'variant_id': 'rs1439050', 'gene': 'NTRK2', 'allele': 'G'},\n", + " {'variant_id': 'rs4675690', 'gene': 'CREB1', 'allele': 'C'},\n", + " {'variant_id': 'rs1147198', 'gene': 'NTRK2', 'allele': 'G'}],\n", + " 'PMC3657875': [{'variant_id': 'rs1801265', 'gene': 'DPYD', 'allele': 'G'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'T'}],\n", + " 'PMC3629689': [{'variant_id': 'rs7900194', 'gene': 'CYP2C9', 'allele': 'A'},\n", + " {'variant_id': 'rs7900194', 'gene': 'CYP2C9', 'allele': 'A'},\n", + " {'variant_id': 'rs9332094', 'gene': 'CYP2C9', 'allele': 'C'},\n", + " {'variant_id': 'rs4918758', 'gene': 'CYP2C9', 'allele': 'C'}],\n", + " 'PMC5652844': [{'variant_id': 'rs2290344', 'gene': 'PIGB', 'allele': 'C'},\n", + " {'variant_id': 'rs2290344', 'gene': 'PIGB', 'allele': 'C'},\n", + " {'variant_id': 'rs2290344', 'gene': 'PIGB', 'allele': 'C'},\n", + " {'variant_id': 'rs8024695', 'gene': 'PIGB', 'allele': 'C'},\n", + " {'variant_id': 'rs12050885', 'gene': 'RAB27A', 'allele': 'G'},\n", + " {'variant_id': 'rs12050885', 'gene': 'RAB27A', 'allele': 'G'},\n", + " {'variant_id': 'rs4896870', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs4896870', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs8024695', 'gene': 'PIGB', 'allele': 'C'},\n", + " {'variant_id': 'rs2607659', 'gene': 'RRM2B', 'allele': 'C'},\n", + " {'variant_id': 'rs2607659', 'gene': 'RRM2B', 'allele': 'C'},\n", + " {'variant_id': 'rs2595500', 'gene': 'ZNF215', 'allele': 'G'},\n", + " {'variant_id': 'rs12050587', 'gene': 'PIGB', 'allele': 'G'},\n", + " {'variant_id': 'rs12050587', 'gene': 'PIGB', 'allele': 'G'},\n", + " {'variant_id': 'rs11636687', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs11636687', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs4261468', 'gene': 'RAB27A', 'allele': 'G'},\n", + " {'variant_id': 'rs4261468', 'gene': 'RAB27A', 'allele': 'G'},\n", + " {'variant_id': 'rs9514827', 'gene': 'TNFSF13B', 'allele': 'C'},\n", + " {'variant_id': 'rs9514827', 'gene': 'TNFSF13B', 'allele': 'C'}],\n", + " 'PMC10912014': [{'variant_id': 'rs1478947',\n", + " 'gene': None,\n", + " 'allele': 'CT + TT'}],\n", + " 'PMC2874659': [{'variant_id': 'rs267606617',\n", + " 'gene': 'MT-RNR1',\n", + " 'allele': 'G'},\n", + " {'variant_id': 'rs267606619', 'gene': 'MT-RNR1', 'allele': 'T'}],\n", + " 'PMC1484504': [{'variant_id': 'rs267606617',\n", + " 'gene': 'MT-RNR1',\n", + " 'allele': 'G'},\n", + " {'variant_id': 'rs267606618', 'gene': 'MT-RNR1', 'allele': 'C'}],\n", + " 'PMC11035779': [{'variant_id': 'rs28929474',\n", + " 'gene': 'SERPINA1',\n", + " 'allele': 'T'}],\n", + " 'PMC4024959': [{'variant_id': 'rs1927911',\n", + " 'gene': 'TLR4',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC3579261': [{'variant_id': 'rs17868320', 'gene': 'UGT1A9', 'allele': 'CC'},\n", + " {'variant_id': 'rs6714486', 'gene': 'UGT1A9', 'allele': 'AA'},\n", + " {'variant_id': 'rs10187694', 'gene': 'UGT1A10', 'allele': 'GG'},\n", + " {'variant_id': 'rs17863762', 'gene': 'UGT1A8', 'allele': 'GG'},\n", + " {'variant_id': 'rs1042597', 'gene': 'UGT1A8', 'allele': 'CC'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'CC'},\n", + " {'variant_id': 'rs7439366', 'gene': 'UGT2B7', 'allele': 'CC'},\n", + " {'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'CC'},\n", + " {'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'GG'}],\n", + " 'PMC3556904': [{'variant_id': 'rs5996696',\n", + " 'gene': 'ADORA2A',\n", + " 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs71651683', 'gene': 'ADORA2A', 'allele': 'CT'},\n", + " {'variant_id': 'rs762551', 'gene': 'CYP1A2', 'allele': 'CC'},\n", + " {'variant_id': 'rs2470890', 'gene': 'CYP1A2', 'allele': 'CC'},\n", + " {'variant_id': 'rs2472304', 'gene': 'CYP1A2', 'allele': 'GG'}],\n", + " 'PMC7197559': [{'variant_id': 'rs12252', 'gene': 'IFITM3', 'allele': 'GG'}],\n", + " 'PMC5574174': [{'variant_id': 'rs3793790', 'gene': 'CHAT', 'allele': 'G'},\n", + " {'variant_id': 'rs11101191', 'gene': 'CHAT', 'allele': 'C'},\n", + " {'variant_id': 'rs867687', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs4838547', 'gene': 'CHAT', 'allele': 'C'}],\n", + " 'PMC6360833': [{'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'T'}],\n", + " 'PMC6198009': [{'variant_id': 'rs1042713', 'gene': 'ADRB2', 'allele': 'AA'},\n", + " {'variant_id': 'rs1042714', 'gene': 'ADRB2', 'allele': 'CG + GG'},\n", + " {'variant_id': 'rs2230349', 'gene': 'GRK5', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs2230345', 'gene': 'GRK5', 'allele': 'AA'},\n", + " {'variant_id': 'rs1042713', 'gene': 'ADRB2', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1042713', 'gene': 'ADRB2', 'allele': 'G'}],\n", + " 'PMC3555060': [{'variant_id': 'rs9934438', 'gene': 'VKORC1', 'allele': 'AA'}],\n", + " 'PMC1716149': [{'variant_id': 'rs1800559', 'gene': 'CACNA1S', 'allele': 'T'}],\n", + " 'PMC4137825': [{'variant_id': 'rs2273697', 'gene': 'ABCC2', 'allele': 'AA'},\n", + " {'variant_id': 'rs3740066', 'gene': 'ABCC2', 'allele': 'TT'},\n", + " {'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'TT'}],\n", + " 'PMC507468': [{'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC2759850': [{'variant_id': 'rs2072661', 'gene': 'CHRNB2', 'allele': 'GG'}],\n", + " 'PMC1376943': [{'variant_id': 'rs118192177', 'gene': 'RYR1', 'allele': 'CT'},\n", + " {'variant_id': 'rs118192175', 'gene': 'RYR1', 'allele': 'CT'},\n", + " {'variant_id': 'rs118192163', 'gene': 'RYR1', 'allele': 'AG'},\n", + " {'variant_id': 'rs118192176', 'gene': 'RYR1', 'allele': 'AG'},\n", + " {'variant_id': 'rs118192172', 'gene': 'RYR1', 'allele': 'CT'},\n", + " {'variant_id': 'rs193922772', 'gene': 'RYR1', 'allele': 'GT'},\n", + " {'variant_id': 'rs118192175', 'gene': 'RYR1', 'allele': 'CT'},\n", + " {'variant_id': 'rs118192176', 'gene': 'RYR1', 'allele': 'AG'},\n", + " {'variant_id': 'rs28933397', 'gene': 'RYR1', 'allele': 'CT'},\n", + " {'variant_id': 'rs118192177', 'gene': 'RYR1', 'allele': 'CT'},\n", + " {'variant_id': 'rs193922747', 'gene': 'RYR1', 'allele': 'CT'},\n", + " {'variant_id': 'rs118192161', 'gene': 'RYR1', 'allele': 'CT'},\n", + " {'variant_id': 'rs121918592', 'gene': 'RYR1', 'allele': 'AG'},\n", + " {'variant_id': 'rs193922770', 'gene': 'RYR1', 'allele': 'CT'}],\n", + " 'PMC3867542': [{'variant_id': 'rs1042114', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs2236861', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs569356', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs529520', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs581111', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs680090', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs12749204', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs2298895', 'gene': 'OPRD1', 'allele': 'T'},\n", + " {'variant_id': 'rs508448', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs4654327', 'gene': 'OPRD1', 'allele': 'G'},\n", + " {'variant_id': 'rs204076', 'gene': 'OPRD1', 'allele': 'T'},\n", + " {'variant_id': 'rs204069', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs204055', 'gene': 'OPRD1', 'allele': 'T'},\n", + " {'variant_id': 'rs2236857', 'gene': 'OPRD1', 'allele': 'C'},\n", + " {'variant_id': 'rs2236855', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs2298897', 'gene': 'OPRD1', 'allele': 'C'},\n", + " {'variant_id': 'rs3766951', 'gene': 'OPRD1', 'allele': 'C'},\n", + " {'variant_id': 'rs760589', 'gene': 'OPRD1', 'allele': 'A'},\n", + " {'variant_id': 'rs204047', 'gene': 'OPRD1', 'allele': 'T'},\n", + " {'variant_id': 'rs678849', 'gene': 'OPRD1', 'allele': 'C'},\n", + " {'variant_id': 'rs419335', 'gene': 'OPRD1', 'allele': 'G'}],\n", + " 'PMC4271081': [{'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs55886062', 'gene': 'DPYD', 'allele': 'AC'},\n", + " {'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'}],\n", + " 'PMC3572125': [{'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs662', 'gene': 'PON1', 'allele': 'CT + TT'}],\n", + " 'PMC4296254': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC3896768': [{'variant_id': 'rs118192172', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs118192176', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs118192177', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs121918593', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs121918592', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs193922748', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs193922807', 'gene': 'RYR1', 'allele': 'C'},\n", + " {'variant_id': 'rs193922802', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs112563513', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs118192122', 'gene': 'RYR1', 'allele': 'A'},\n", + " {'variant_id': 'rs193922816', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs28933397', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs193922772', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs193922764', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs118192124', 'gene': 'RYR1', 'allele': 'T'},\n", + " {'variant_id': 'rs118192168', 'gene': 'RYR1', 'allele': 'A'}],\n", + " 'PMC5432027': [{'variant_id': 'rs1142345', 'gene': 'TPMT', 'allele': 'TT'},\n", + " {'variant_id': 'rs12201199', 'gene': 'TPMT', 'allele': 'AA'},\n", + " {'variant_id': 'rs9332377', 'gene': 'COMT', 'allele': 'CC'},\n", + " {'variant_id': 'rs4646316', 'gene': 'COMT', 'allele': 'CC'},\n", + " {'variant_id': 'rs1800460', 'gene': 'TPMT', 'allele': 'CC'},\n", + " {'variant_id': 'rs1872328', 'gene': 'ACYP2', 'allele': 'AA + AG'}],\n", + " 'PMC4034115': [{'variant_id': 'rs5326', 'gene': 'DRD1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs4867798', 'gene': 'DRD1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs6280', 'gene': 'DRD3', 'allele': 'TT'},\n", + " {'variant_id': 'rs11246226', 'gene': 'DRD4', 'allele': 'AA'},\n", + " {'variant_id': 'rs1800544', 'gene': 'ADRA2A', 'allele': 'CC'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs6280', 'gene': 'DRD3', 'allele': 'CT + TT'}],\n", + " 'PMC4131016': [{'variant_id': 'rs9479757', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs9479757', 'gene': 'OPRM1', 'allele': 'GG'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs3798676', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs4870266', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs483481', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs569284', 'gene': 'OPRM1', 'allele': 'C'},\n", + " {'variant_id': 'rs9371776', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs2010884', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs13195018', 'gene': 'OPRM1', 'allele': 'C'},\n", + " {'variant_id': 'rs518596', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs9397687', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs73576470', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs7763748', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs6557337', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs527434', 'gene': 'OPRM1', 'allele': 'C'},\n", + " {'variant_id': 'rs610231', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs9371773', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs9384174', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs2075572', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs3798683', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs632499', 'gene': 'OPRM1', 'allele': 'A'},\n", + " {'variant_id': 'rs9397685', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs3798688', 'gene': 'OPRM1', 'allele': 'T'}],\n", + " 'PMC3242306': [{'variant_id': 'rs1799971',\n", + " 'gene': 'OPRM1',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG + GG'}],\n", + " 'PMC5464525': [{'variant_id': 'rs142427338',\n", + " 'gene': 'ALDH1B1',\n", + " 'allele': 'T'},\n", + " {'variant_id': 'rs2073478', 'gene': 'ALDH1B1', 'allele': 'G'},\n", + " {'variant_id': 'rs4878199', 'gene': 'ALDH1B1', 'allele': 'G'},\n", + " {'variant_id': 'rs2228093', 'gene': 'ALDH1B1', 'allele': 'T'}],\n", + " 'PMC4900521': [{'variant_id': 'rs4147581', 'gene': 'GSTP1', 'allele': 'GG'},\n", + " {'variant_id': 'rs1695', 'gene': 'GSTP1', 'allele': 'AA'}],\n", + " 'PMC3907445': [{'variant_id': 'rs3787140', 'gene': 'CHRNA4', 'allele': 'CC'},\n", + " {'variant_id': 'rs6090378', 'gene': 'CHRNA4', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs3787138', 'gene': 'CHRNA4', 'allele': 'GG'}],\n", + " 'PMC3940691': [{'variant_id': 'rs4148354',\n", + " 'gene': 'ABCC1',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs2889517', 'gene': 'ABCC1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs11861115', 'gene': 'ABCC1', 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs16967126', 'gene': 'ABCC1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs4148350', 'gene': 'ABCC1', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs8187996', 'gene': 'ALDH1A1', 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs3764435', 'gene': 'ALDH1A1', 'allele': 'AA + AC'},\n", + " {'variant_id': 'rs63319', 'gene': 'ALDH1A1', 'allele': 'GT + TT'},\n", + " {'variant_id': 'rs35596', 'gene': 'ABCC1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs903880', 'gene': 'ABCC1', 'allele': 'AA + AC'},\n", + " {'variant_id': 'rs3764435', 'gene': 'ALDH1A1', 'allele': 'A'},\n", + " {'variant_id': 'rs168351', 'gene': 'ALDH1A1', 'allele': 'A'}],\n", + " 'PMC3899627': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs3852209', 'gene': 'HINT1', 'allele': 'CC'},\n", + " {'variant_id': 'rs3786047', 'gene': 'ARRB2', 'allele': 'A'},\n", + " {'variant_id': 'rs1045280', 'gene': 'ARRB2', 'allele': 'C'},\n", + " {'variant_id': 'rs2036657', 'gene': 'ARRB2', 'allele': 'G'},\n", + " {'variant_id': 'rs2278060', 'gene': 'HINT1', 'allele': 'C'}],\n", + " 'PMC11139312': [{'variant_id': 'rs2228570', 'gene': 'VDR', 'allele': 'A'},\n", + " {'variant_id': 'rs1544410', 'gene': 'VDR', 'allele': 'CC'}],\n", + " 'PMC11457605': [{'variant_id': 'rs6809699', 'gene': 'P2RY12', 'allele': 'A'},\n", + " {'variant_id': 'rs2046934', 'gene': 'P2RY12', 'allele': 'A'},\n", + " {'variant_id': 'rs6801273', 'gene': 'P2RY12', 'allele': 'TT'}],\n", + " 'PMC11492648': [{'variant_id': 'rs352139',\n", + " 'gene': 'ALAS1',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC6509985': [{'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'AG'}],\n", + " 'PMC11050844': [{'variant_id': 'rs717620', 'gene': 'ABCC2', 'allele': 'C'}],\n", + " 'PMC8238023': [{'variant_id': 'CYP2B6*6',\n", + " 'gene': 'CYP2B6',\n", + " 'allele': '*6/*6'},\n", + " {'variant_id': 'rs1038376', 'gene': 'CYP2B6', 'allele': 'TT'},\n", + " {'variant_id': 'rs2279343', 'gene': 'CYP2B6', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs11882424', 'gene': 'CYP2B6', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs10500282', 'gene': 'CYP2B6', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs4803419', 'gene': 'CYP2B6', 'allele': 'TT'}],\n", + " 'PMC2874321': [{'variant_id': 'rs16969968', 'gene': 'CHRNA5', 'allele': 'A'},\n", + " {'variant_id': 'rs578776', 'gene': 'CHRNA3', 'allele': 'A'}],\n", + " 'PMC5037629': [{'variant_id': 'rs10946739', 'gene': 'RIPOR2', 'allele': 'C'},\n", + " {'variant_id': 'rs393994', 'gene': 'AGBL4', 'allele': 'A'},\n", + " {'variant_id': 'rs320003', 'gene': 'AGBL4', 'allele': 'G'},\n", + " {'variant_id': 'rs7958375', 'gene': 'CUX2', 'allele': 'G'},\n", + " {'variant_id': 'rs10946737', 'gene': 'RIPOR2', 'allele': 'A'},\n", + " {'variant_id': 'rs319952', 'gene': 'AGBL4', 'allele': 'A'}],\n", + " 'PMC3055457': [{'variant_id': 'rs948854', 'gene': 'GAL', 'allele': 'C'},\n", + " {'variant_id': 'rs948854', 'gene': 'GAL', 'allele': 'C'}],\n", + " 'PMC8159730': [{'variant_id': 'rs4149056',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC + CT'},\n", + " {'variant_id': 'rs2306283', 'gene': 'SLCO1B1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs4149056', 'gene': 'SLCO1B1', 'allele': 'CC + CT'}],\n", + " 'PMC4221198': [{'variant_id': 'rs1047626', 'gene': 'SLC30A9', 'allele': 'A'},\n", + " {'variant_id': 'rs7862221', 'gene': 'TSC1', 'allele': 'C'},\n", + " {'variant_id': 'rs11819745', 'gene': 'THRA', 'allele': 'G'},\n", + " {'variant_id': 'rs3818822', 'gene': 'CHIA', 'allele': 'A'},\n", + " {'variant_id': 'rs1074373', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs3130100', 'gene': 'ZBTB22', 'allele': 'C'},\n", + " {'variant_id': 'rs3097671', 'gene': 'HLA-DPB1', 'allele': 'C'},\n", + " {'variant_id': 'rs3129294', 'gene': 'HLA-DPB2', 'allele': 'C'},\n", + " {'variant_id': 'rs1042151', 'gene': 'HLA-DPB1', 'allele': 'G'},\n", + " {'variant_id': 'rs1042136', 'gene': 'HLA-DPB1', 'allele': 'C'}],\n", + " 'PMC7674154': [{'variant_id': 'rs2253201', 'gene': 'KCNMA1', 'allele': 'G'}],\n", + " 'PMC2799180': [{'variant_id': 'rs7903146', 'gene': 'TCF7L2', 'allele': 'T'}],\n", + " 'PMC3085371': [{'variant_id': 'rs1803274', 'gene': 'BCHE', 'allele': 'T'},\n", + " {'variant_id': 'rs1799807', 'gene': 'BCHE', 'allele': 'C'}],\n", + " 'PMC9875006': [{'variant_id': 'rs3824662', 'gene': 'GATA3', 'allele': 'AA'},\n", + " {'variant_id': 'rs3824662', 'gene': 'GATA3', 'allele': 'AA'}],\n", + " 'PMC4469933': [{'variant_id': 'rs510769', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs510769', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs10485057', 'gene': 'OPRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs10485057', 'gene': 'OPRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs10485057', 'gene': 'OPRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs10485057', 'gene': 'OPRM1', 'allele': 'AA'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs510769', 'gene': 'OPRM1', 'allele': 'T'},\n", + " {'variant_id': 'rs510769', 'gene': 'OPRM1', 'allele': 'T'}],\n", + " 'PMC7485556': [{'variant_id': 'rs2533200', 'gene': 'DPP6', 'allele': 'C'},\n", + " {'variant_id': 'rs7900002', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2582405', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs56722963', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs576859', 'gene': 'MED19, TMX2', 'allele': 'A'},\n", + " {'variant_id': 'rs6589386', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs13125415', 'gene': 'ADH1C', 'allele': 'A'},\n", + " {'variant_id': 'rs13135092', 'gene': 'SLC39A8', 'allele': 'A'},\n", + " {'variant_id': 'rs1229984', 'gene': 'ADH1B', 'allele': 'T'},\n", + " {'variant_id': 'rs9679319', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs1402398', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs13382553', 'gene': 'THSD7B', 'allele': 'A'},\n", + " {'variant_id': 'rs2673136', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs13129401', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs62250713', 'gene': 'CADM2', 'allele': 'A'},\n", + " {'variant_id': 'rs75967634', 'gene': 'ADH1B', 'allele': 'T'},\n", + " {'variant_id': 'rs6421482', 'gene': 'PDE4B', 'allele': 'A'},\n", + " {'variant_id': 'rs61767420', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs1260326', 'gene': 'GCKR', 'allele': 'T'},\n", + " {'variant_id': 'rs494904', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs492602', 'gene': 'FUT2', 'allele': 'A'},\n", + " {'variant_id': 'rs9937709', 'gene': 'FTO', 'allele': 'A'},\n", + " {'variant_id': 'rs1783835', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs12296477', 'gene': 'SLC4A8', 'allele': 'C'},\n", + " {'variant_id': 'rs61974485', 'gene': 'ARID4A', 'allele': 'T'},\n", + " {'variant_id': 'rs8008020', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs72768626', 'gene': 'TNRC6A', 'allele': 'A'}],\n", + " 'PMC11149109': [{'variant_id': 'rs1883112', 'gene': 'NCF4', 'allele': 'AA'},\n", + " {'variant_id': 'rs1800871', 'gene': 'IL10', 'allele': 'AG + GG'}],\n", + " 'PMC5899062': [{'variant_id': 'rs3828743', 'gene': 'TSPYL1', 'allele': 'A'},\n", + " {'variant_id': 'rs3828743', 'gene': 'TSPYL1', 'allele': 'AA'}],\n", + " 'PMC4462564': [{'variant_id': 'rs1051730', 'gene': 'CHRNA3', 'allele': 'A'},\n", + " {'variant_id': 'rs1051730', 'gene': 'CHRNA3', 'allele': 'A'}],\n", + " 'PMC5541380': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'AG + GG'}],\n", + " 'PMC9450009': [{'variant_id': 'rs4880', 'gene': 'SOD2', 'allele': 'GG'},\n", + " {'variant_id': 'rs4880', 'gene': 'SOD2', 'allele': 'GG'}],\n", + " 'PMC11734037': [{'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'AG'}],\n", + " 'PMC4039076': [{'variant_id': 'rs3824662', 'gene': 'GATA3', 'allele': 'A'},\n", + " {'variant_id': 'rs3824662', 'gene': 'GATA3', 'allele': 'A'}],\n", + " 'PMC5161051': [{'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'}],\n", + " 'PMC8449801': [{'variant_id': 'rs62293499', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs1877193', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs6735923', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs62293499', 'gene': 'CLDN11', 'allele': 'C'},\n", + " {'variant_id': 'rs6735923', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs1877193', 'gene': None, 'allele': 'C'}],\n", + " 'PMC11655210': [{'variant_id': 'rs4294451',\n", + " 'gene': 'DPYD',\n", + " 'allele': 'AT + TT'},\n", + " {'variant_id': 'rs4294451', 'gene': 'DPYD', 'allele': 'AT + TT'}],\n", + " 'PMC3410623': [{'variant_id': 'rs17135437', 'gene': 'COL26A1', 'allele': 'T'},\n", + " {'variant_id': 'rs16965962', 'gene': None, 'allele': 'A'}],\n", + " 'PMC2698708': [{'variant_id': 'rs1042714', 'gene': 'ADRB2', 'allele': 'GG'},\n", + " {'variant_id': 'rs1042714', 'gene': 'ADRB2', 'allele': 'GG'}],\n", + " 'PMC11824427': [{'variant_id': 'rs4982753',\n", + " 'gene': 'SLC22A17',\n", + " 'allele': 'T'},\n", + " {'variant_id': 'rs7319981', 'gene': 'SLC10A2', 'allele': 'A'},\n", + " {'variant_id': 'rs2232228', 'gene': 'HAS3', 'allele': 'A'},\n", + " {'variant_id': 'rs2229774', 'gene': 'RARG', 'allele': 'A'}],\n", + " 'PMC5396392': [{'variant_id': 'rs1800497',\n", + " 'gene': 'ANKK1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs2283265', 'gene': 'DRD2', 'allele': 'AA + AC'}],\n", + " 'PMC3920286': [{'variant_id': 'rs1045280', 'gene': 'ARRB2', 'allele': 'C'},\n", + " {'variant_id': 'rs2271167', 'gene': 'ARRB2', 'allele': 'A'},\n", + " {'variant_id': 'rs2036657', 'gene': 'ARRB2', 'allele': 'G'},\n", + " {'variant_id': 'rs3786047', 'gene': 'ARRB2', 'allele': 'A'},\n", + " {'variant_id': 'rs4522461', 'gene': 'ARRB2', 'allele': 'T'},\n", + " {'variant_id': 'rs1045280', 'gene': 'ARRB2', 'allele': 'C'},\n", + " {'variant_id': 'rs2271167', 'gene': 'ARRB2', 'allele': 'A'},\n", + " {'variant_id': 'rs2036657', 'gene': 'ARRB2', 'allele': 'G'},\n", + " {'variant_id': 'rs3786047', 'gene': 'ARRB2', 'allele': 'A'},\n", + " {'variant_id': 'rs4522461', 'gene': 'ARRB2', 'allele': 'T'}],\n", + " 'PMC10586897': [{'variant_id': 'rs4148323', 'gene': 'UGT1A1', 'allele': 'A'},\n", + " {'variant_id': 'rs7662029', 'gene': 'UGT2B7', 'allele': 'G'},\n", + " {'variant_id': 'rs7439366', 'gene': 'UGT2B7', 'allele': 'C'}],\n", + " 'PMC10786450': [{'variant_id': 'rs4818',\n", + " 'gene': 'COMT',\n", + " 'allele': 'CC + CG'}],\n", + " 'PMC9903350': [{'variant_id': 'rs4244285',\n", + " 'gene': 'CYP2C19',\n", + " 'allele': 'AA + AG'}],\n", + " 'PMC6987567': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC3919513': [{'variant_id': 'rs112561475',\n", + " 'gene': 'UGT2B10',\n", + " 'allele': 'AG + GG'},\n", + " {'variant_id': 'rs61750900', 'gene': 'UGT2B10', 'allele': 'T'}],\n", + " 'PMC2759106': [{'variant_id': 'rs267606617',\n", + " 'gene': 'MT-RNR1',\n", + " 'allele': 'G'}],\n", + " 'PMC11434779': [{'variant_id': 'rs11615',\n", + " 'gene': 'ERCC1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs4793665', 'gene': 'ABCC3', 'allele': 'TT'}],\n", + " 'PMC4975126': [{'variant_id': 'rs4291', 'gene': 'ACE', 'allele': 'AA'}],\n", + " 'PMC3548029': [{'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'},\n", + " {'variant_id': 'rs1799971', 'gene': 'OPRM1', 'allele': 'G'}],\n", + " 'PMC9302309': [{'variant_id': 'rs7606353', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs62283056', 'gene': 'WFS1', 'allele': 'C'}],\n", + " 'PMC9541367': [{'variant_id': 'rs4471527', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs77542827', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs199755581', 'gene': 'NIPAL2', 'allele': 'A'},\n", + " {'variant_id': 'rs77491650', 'gene': 'DDX12P', 'allele': 'C'}],\n", + " 'PMC6166924': [{'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'G'},\n", + " {'variant_id': 'rs16918482', 'gene': 'RRM2B', 'allele': 'C'},\n", + " {'variant_id': 'rs1891643', 'gene': 'SAMHD1', 'allele': 'G'},\n", + " {'variant_id': 'rs8124728', 'gene': 'SAMHD1', 'allele': 'A'},\n", + " {'variant_id': 'rs8187758', 'gene': 'SLC28A1', 'allele': 'A'}],\n", + " 'PMC3700411': [{'variant_id': 'rs1799971',\n", + " 'gene': 'OPRM1',\n", + " 'allele': 'AG + GG'}],\n", + " 'PMC10925634': [{'variant_id': 'rs1045642',\n", + " 'gene': 'ABCB1',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs2032582', 'gene': 'ABCB1', 'allele': 'CC'},\n", + " {'variant_id': 'rs1128503', 'gene': 'ABCB1', 'allele': 'AA + AG'}],\n", + " 'PMC2913951': [{'variant_id': 'rs1870377', 'gene': 'KDR', 'allele': 'T'},\n", + " {'variant_id': 'rs1870377', 'gene': 'KDR', 'allele': 'T'}],\n", + " 'PMC3706515': [{'variant_id': 'rs9552679', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs6988229', 'gene': 'COL22A1', 'allele': 'T'},\n", + " {'variant_id': 'rs17495520', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs1663332', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs1663330', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs17701271', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs518350', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs10511905', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs1522113', 'gene': 'CLOCK', 'allele': 'A'},\n", + " {'variant_id': 'rs1423515', 'gene': None, 'allele': 'A'},\n", + " {'variant_id': 'rs1419555', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs6002674', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs11252394', 'gene': None, 'allele': 'A'}],\n", + " 'PMC3425941': [{'variant_id': 'rs11503014', 'gene': 'GABRA2', 'allele': 'G'},\n", + " {'variant_id': 'rs2236256', 'gene': 'OPRM1', 'allele': 'C'}],\n", + " 'PMC5053704': [{'variant_id': 'rs2070744', 'gene': 'NOS3', 'allele': 'TT'},\n", + " {'variant_id': 'rs1799983', 'gene': 'NOS3', 'allele': 'GG'},\n", + " {'variant_id': 'rs1799983', 'gene': 'NOS3', 'allele': 'GG'},\n", + " {'variant_id': 'rs2070744', 'gene': 'NOS3', 'allele': 'TT'}],\n", + " 'PMC4999337': [{'variant_id': 'rs116855232',\n", + " 'gene': 'NUDT15',\n", + " 'allele': 'TT'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'T'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'T'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'T'},\n", + " {'variant_id': 'rs116855232', 'gene': 'NUDT15', 'allele': 'TT'}],\n", + " 'PMC3667396': [{'variant_id': 'rs37973', 'gene': 'GLCCI1', 'allele': 'GG'}],\n", + " 'PMC10506908': [{'variant_id': 'rs61752783', 'gene': 'POLG', 'allele': 'A'}],\n", + " 'PMC7831885': [{'variant_id': 'rs12132152', 'gene': 'DPYD', 'allele': 'AG'},\n", + " {'variant_id': 'rs115232898', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC3139322': [{'variant_id': 'rs1801086', 'gene': 'RYR1', 'allele': 'C'}],\n", + " 'PMC10933594': [{'variant_id': 'rs2257212',\n", + " 'gene': 'SLC15A2',\n", + " 'allele': 'T'}],\n", + " 'PMC3235081': [{'variant_id': 'rs396991', 'gene': 'FCGR3A', 'allele': 'A'},\n", + " {'variant_id': 'rs9344', 'gene': 'CCND1', 'allele': 'G'}],\n", + " 'PMC4980282': [{'variant_id': 'rs6461639', 'gene': 'RAPGEF5', 'allele': 'T'},\n", + " {'variant_id': 'rs6461639', 'gene': 'RAPGEF5', 'allele': 'TT'}],\n", + " 'PMC3967864': [{'variant_id': 'rs11568817',\n", + " 'gene': 'HTR1B',\n", + " 'allele': 'AC + CC'},\n", + " {'variant_id': 'rs6311', 'gene': 'HTR2A', 'allele': 'CT + TT'}],\n", + " 'PMC2850440': [{'variant_id': 'rs17234657', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs11805303', 'gene': 'IL23R', 'allele': 'T'},\n", + " {'variant_id': 'rs10033464', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2200733', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs2104286', 'gene': 'IL2RA', 'allele': 'T'},\n", + " {'variant_id': 'rs6679677', 'gene': 'PHTF1, RSBN1', 'allele': 'A'},\n", + " {'variant_id': 'rs3135388', 'gene': 'HLA-DRA', 'allele': 'A'},\n", + " {'variant_id': 'rs6897932', 'gene': 'IL7R', 'allele': 'T'},\n", + " {'variant_id': 'rs2542151', 'gene': None, 'allele': 'G'},\n", + " {'variant_id': 'rs17221417', 'gene': 'NOD2', 'allele': 'G'},\n", + " {'variant_id': 'rs1000113', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs8050136', 'gene': 'FTO', 'allele': 'A'},\n", + " {'variant_id': 'rs5219', 'gene': 'KCNJ11', 'allele': 'T'},\n", + " {'variant_id': 'rs5215', 'gene': 'KCNJ11', 'allele': 'C'},\n", + " {'variant_id': 'rs12255372', 'gene': 'TCF7L2', 'allele': 'T'},\n", + " {'variant_id': 'rs12243326', 'gene': 'TCF7L2', 'allele': 'C'},\n", + " {'variant_id': 'rs4506565', 'gene': 'TCF7L2', 'allele': 'T'},\n", + " {'variant_id': 'rs10811661', 'gene': None, 'allele': 'C'},\n", + " {'variant_id': 'rs4402960', 'gene': 'IGF2BP2', 'allele': 'T'},\n", + " {'variant_id': 'rs2476601', 'gene': 'PTPN22', 'allele': 'A'}],\n", + " 'PMC3360116': [{'variant_id': 'rs7297610', 'gene': 'YEATS4', 'allele': 'CC'},\n", + " {'variant_id': 'rs7297610', 'gene': 'YEATS4', 'allele': 'CC'},\n", + " {'variant_id': 'rs7297610', 'gene': 'YEATS4', 'allele': 'CC'}],\n", + " 'PMC11534822': [{'variant_id': 'rs924607', 'gene': 'CEP72', 'allele': 'TT'}],\n", + " 'PMC10416089': [{'variant_id': 'rs12054895', 'gene': None, 'allele': 'T'},\n", + " {'variant_id': 'rs12054895', 'gene': None, 'allele': 'T'}],\n", + " 'PMC11583987': [{'variant_id': 'rs71647871', 'gene': 'CES1', 'allele': 'CT'}],\n", + " 'PMC2696936': [{'variant_id': 'rs267606617',\n", + " 'gene': 'MT-RNR1',\n", + " 'allele': 'G'}],\n", + " 'PMC8953705': [{'variant_id': 'rs1800629', 'gene': 'TNF', 'allele': 'GG'},\n", + " {'variant_id': 'rs1800797', 'gene': 'IL6', 'allele': 'A'},\n", + " {'variant_id': 'rs1800797', 'gene': 'IL6', 'allele': 'AG + GG'}],\n", + " 'PMC6612573': [{'variant_id': 'rs1229984', 'gene': 'ADH1B', 'allele': 'T'},\n", + " {'variant_id': 'rs61826952', 'gene': 'RABGAP1L', 'allele': 'G'}],\n", + " 'PMC3031788': [{'variant_id': 'rs10965219',\n", + " 'gene': 'CDKN2B-AS1',\n", + " 'allele': 'G'},\n", + " {'variant_id': 'rs10965219', 'gene': 'CDKN2B-AS1', 'allele': 'G'}],\n", + " 'PMC3690109': [{'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'AA'},\n", + " {'variant_id': 'rs4680', 'gene': 'COMT', 'allele': 'AA'}],\n", + " 'PMC11082567': [{'variant_id': 'rs1065852', 'gene': 'CYP2D6', 'allele': 'AA'},\n", + " {'variant_id': 'rs16947', 'gene': 'CYP2D6', 'allele': 'GG'},\n", + " {'variant_id': 'rs16947', 'gene': 'CYP2D6', 'allele': 'GG'},\n", + " {'variant_id': 'rs1065852', 'gene': 'CYP2D6', 'allele': 'AA'},\n", + " {'variant_id': 'rs1065852', 'gene': 'CYP2D6', 'allele': 'AA'},\n", + " {'variant_id': 'rs16947', 'gene': 'CYP2D6', 'allele': 'GG'}],\n", + " 'PMC3269589': [{'variant_id': 'rs4149056',\n", + " 'gene': 'SLCO1B1',\n", + " 'allele': 'CT + TT'}],\n", + " 'PMC8019867': [{'variant_id': 'rs1801131', 'gene': 'MTHFR', 'allele': 'G'},\n", + " {'variant_id': 'rs1801133', 'gene': 'MTHFR', 'allele': 'A'}],\n", + " 'PMC11106472': [{'variant_id': 'rs10849757',\n", + " 'gene': 'COQ5',\n", + " 'allele': 'AA + AG'},\n", + " {'variant_id': 'rs11548336', 'gene': 'COQ3', 'allele': 'TT'},\n", + " {'variant_id': 'rs4693075', 'gene': 'COQ2', 'allele': 'CG + GG'}],\n", + " 'PMC7718413': [{'variant_id': 'rs74497159',\n", + " 'gene': None,\n", + " 'allele': 'GG + GT'}],\n", + " 'PMC2940808': [{'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'}],\n", + " 'PMC3029225': [{'variant_id': 'rs267606618',\n", + " 'gene': 'MT-RNR1',\n", + " 'allele': 'C'},\n", + " {'variant_id': 'rs267606619', 'gene': 'MT-RNR1', 'allele': 'T'},\n", + " {'variant_id': 'rs267606617', 'gene': 'MT-RNR1', 'allele': 'G'}],\n", + " 'PMC3070684': [{'variant_id': 'rs67376798', 'gene': 'DPYD', 'allele': 'AT'},\n", + " {'variant_id': 'rs3918290', 'gene': 'DPYD', 'allele': 'CT'},\n", + " {'variant_id': 'rs55886062', 'gene': 'DPYD', 'allele': 'AC'}],\n", + " 'PMC2844036': [{'variant_id': 'rs2032582',\n", + " 'gene': 'ABCB1',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs3211371', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs12721655', 'gene': 'CYP2B6', 'allele': 'AG'},\n", + " {'variant_id': 'rs4244285', 'gene': 'CYP2C19', 'allele': 'AA'},\n", + " {'variant_id': 'rs8192709', 'gene': 'CYP2B6', 'allele': 'CT'},\n", + " {'variant_id': 'rs714368', 'gene': 'SLC22A16', 'allele': 'C'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs8192709', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs12210538', 'gene': 'SLC22A16', 'allele': 'G'},\n", + " {'variant_id': 'rs723685', 'gene': 'SLC22A16', 'allele': 'G'},\n", + " {'variant_id': 'rs6907567', 'gene': 'SLC22A16', 'allele': 'G'}],\n", + " 'PMC2842298': [{'variant_id': 'rs8014194', 'gene': 'CLMN', 'allele': 'A'}],\n", + " 'PMC8442489': [{'variant_id': 'rs2076295', 'gene': 'DSP', 'allele': 'TT'}],\n", + " 'PMC10139887': [{'variant_id': 'rs2231137',\n", + " 'gene': 'ABCG2',\n", + " 'allele': 'CT + TT'},\n", + " {'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'GG'}],\n", + " 'PMC2933581': [{'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'TT'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'TT'}],\n", + " 'PMC11357698': [{'variant_id': 'rs73800947', 'gene': 'GABRP', 'allele': 'T'},\n", + " {'variant_id': 'rs35719165', 'gene': 'SEMA5A', 'allele': 'A'},\n", + " {'variant_id': 'rs34234515', 'gene': 'SLCO1B1', 'allele': 'A'},\n", + " {'variant_id': 'rs1862167', 'gene': 'EDIL3', 'allele': 'T'},\n", + " {'variant_id': 'rs12913535', 'gene': 'SMAD3', 'allele': 'T'}],\n", + " 'PMC1729671': [{'variant_id': 'rs5186', 'gene': 'AGTR1', 'allele': 'CC'}],\n", + " 'PMC8872593': [{'variant_id': 'rs10821936', 'gene': 'ARID5B', 'allele': 'TT'},\n", + " {'variant_id': 'rs2372536', 'gene': 'ATIC', 'allele': 'G'}],\n", + " 'PMC1874364': [{'variant_id': 'rs1045642', 'gene': 'ABCB1', 'allele': 'AA'}],\n", + " 'PMC3614365': [{'variant_id': 'rs28399499', 'gene': 'CYP2B6', 'allele': 'C'},\n", + " {'variant_id': 'rs4803419', 'gene': 'CYP2B6', 'allele': 'T'},\n", + " {'variant_id': 'rs3745274', 'gene': 'CYP2B6', 'allele': 'T'}],\n", + " 'PMC7655616': [{'variant_id': 'rs4939827', 'gene': 'SMAD7', 'allele': 'CC'},\n", + " {'variant_id': 'rs3812863', 'gene': 'CDX2', 'allele': 'GG'}],\n", + " ...}" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "true_variant_list_json" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "# save true variant list to file\n", + "json.dump(true_variant_list_json, open(\"data/benchmark/true_variant_list.json\", \"w\"), indent=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare extract variants list to true_variant_list" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "from src.annotation_extraction.utils import compare_lists" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================= PMC6714673 =================\n", + "Experimental List:\n", + "\u001b[31mrs1057910\u001b[0m \u001b[31mrs1799853\u001b[0m \u001b[31mrs9923231\u001b[0m \u001b[31mrs1043550\u001b[0m \u001b[31mrs12714145\u001b[0m \u001b[31mrs1051740\u001b[0m \u001b[31mrs2108622\u001b[0m \u001b[31mrs6046\u001b[0m \u001b[31mrs11676382\u001b[0m \u001b[31mrs699664\u001b[0m \u001b[31mrs1799853\u001b[0m \u001b[31mrs1057910\u001b[0m \u001b[31mrs11676382\u001b[0m \u001b[31mrs6046\u001b[0m \u001b[31mrs2108622\u001b[0m \u001b[31mrs699664\u001b[0m \u001b[31mrs1043550\u001b[0m \u001b[31mrs12714145\u001b[0m \u001b[31mrs1051740\u001b[0m \u001b[31mrs9923231\u001b[0m\n", + "\n", + "Ground Truth List:\n", + "\u001b[31mrs113993960\u001b[0m \u001b[31mCYP2C19*1\u001b[0m \u001b[31mCYP2C19*2\u001b[0m \u001b[31mCYP2C19*3\u001b[0m \u001b[31mCYP2C19*4\u001b[0m \u001b[31mCYP2C19*5\u001b[0m \u001b[31mCYP2C19*6\u001b[0m \u001b[31mCYP2C19*7\u001b[0m \u001b[31mCYP2C19*8\u001b[0m \u001b[31mCYP2C19*9\u001b[0m \u001b[31mCYP2C19*10\u001b[0m \u001b[31mCYP2C19*11\u001b[0m \u001b[31mCYP2C19*12\u001b[0m \u001b[31mCYP2C19*13\u001b[0m \u001b[31mCYP2C19*14\u001b[0m \u001b[31mCYP2C19*15\u001b[0m \u001b[31mCYP2C19*16\u001b[0m \u001b[31mCYP2C19*17\u001b[0m \u001b[31mCYP2C19*18\u001b[0m \u001b[31mCYP2C19*19\u001b[0m \u001b[31mCYP2C19*20\u001b[0m \u001b[31mCYP2C19*21\u001b[0m \u001b[31mCYP2C19*22\u001b[0m \u001b[31mCYP2C19*23\u001b[0m \u001b[31mCYP2C19*24\u001b[0m \u001b[31mCYP2C19*25\u001b[0m \u001b[31mCYP2C19*26\u001b[0m \u001b[31mCYP2C19*27\u001b[0m \u001b[31mCYP2C19*28\u001b[0m \u001b[31mCYP2C19*29\u001b[0m \u001b[31mCYP2C19*30\u001b[0m\n", + "================= PMC3553682 =================\n", + "Experimental List:\n", + "\u001b[31mCYP2B6*6\u001b[0m \u001b[31mCYP2B6*1\u001b[0m \u001b[31mrs8192709\u001b[0m \u001b[31mrs2279343\u001b[0m \u001b[31mrs3745274\u001b[0m \u001b[31mrs3745274\u001b[0m \u001b[31mrs3745274\u001b[0m \u001b[31mrs2279343\u001b[0m \u001b[31mrs3211371\u001b[0m\n", + "\n", + "Ground Truth List:\n", + "\u001b[31mrs113993960\u001b[0m \u001b[31mrs3892097\u001b[0m \u001b[31mCYP2C9*3\u001b[0m \u001b[31mrs1057910\u001b[0m \u001b[31mrs1801133\u001b[0m \u001b[31mrs1800497\u001b[0m \u001b[31mrs2236225\u001b[0m \u001b[31mrs9923231\u001b[0m\n", + "================= PMC4270923 =================\n", + "Experimental List:\n", + "\u001b[31mrs4752292\u001b[0m \u001b[31mrs3740563\u001b[0m \u001b[31mrs10787959\u001b[0m \u001b[31mrs11198893\u001b[0m \u001b[31mrs16947\u001b[0m\n", + "\n", + "Ground Truth List:\n", + "\u001b[31mrs113993960\u001b[0m \u001b[31mCYP2C19*1\u001b[0m \u001b[31mCYP2C19*2\u001b[0m \u001b[31mCYP2C19*3\u001b[0m \u001b[31mCYP2C19*4\u001b[0m \u001b[31mCYP2C19*5\u001b[0m \u001b[31mCYP2C19*6\u001b[0m \u001b[31mCYP2C19*7\u001b[0m \u001b[31mCYP2C19*8\u001b[0m \u001b[31mCYP2C19*9\u001b[0m \u001b[31mCYP2C19*10\u001b[0m \u001b[31mCYP2C19*11\u001b[0m \u001b[31mCYP2C19*12\u001b[0m \u001b[31mCYP2C19*13\u001b[0m \u001b[31mCYP2C19*14\u001b[0m \u001b[31mCYP2C19*15\u001b[0m \u001b[31mCYP2C19*16\u001b[0m \u001b[31mCYP2C19*17\u001b[0m \u001b[31mCYP2C19*18\u001b[0m \u001b[31mCYP2C19*19\u001b[0m \u001b[31mCYP2C19*20\u001b[0m \u001b[31mCYP2C19*21\u001b[0m \u001b[31mCYP2C19*22\u001b[0m \u001b[31mCYP2C19*23\u001b[0m \u001b[31mCYP2C19*24\u001b[0m \u001b[31mCYP2C19*25\u001b[0m \u001b[31mCYP2C19*26\u001b[0m \u001b[31mCYP2C19*27\u001b[0m \u001b[31mCYP2C19*28\u001b[0m \u001b[31mCYP2C19*29\u001b[0m \u001b[31mCYP2C19*30\u001b[0m\n", + "================= PMC4557249 =================\n", + "Experimental List:\n", + "\u001b[31mSLC6A4 HTTLPR long form (L allele), SLC6A4 HTTLPR short form (S allele)\u001b[0m \u001b[31mrs6311\u001b[0m \u001b[31mrs6295\u001b[0m \u001b[31mrs6313\u001b[0m\n", + "\n", + "Ground Truth List:\n", + "\u001b[31mrs113993960\u001b[0m \u001b[31mCYP2C19*1\u001b[0m \u001b[31mCYP2C19*2\u001b[0m \u001b[31mCYP2C19*3\u001b[0m \u001b[31mCYP2C19*4\u001b[0m \u001b[31mCYP2C19*5\u001b[0m \u001b[31mCYP2C19*6\u001b[0m \u001b[31mCYP2C19*7\u001b[0m \u001b[31mCYP2C19*8\u001b[0m \u001b[31mCYP2C19*9\u001b[0m \u001b[31mCYP2C19*10\u001b[0m \u001b[31mCYP2C19*11\u001b[0m \u001b[31mCYP2C19*12\u001b[0m \u001b[31mCYP2C19*13\u001b[0m \u001b[31mCYP2C19*14\u001b[0m \u001b[31mCYP2C19*15\u001b[0m \u001b[31mCYP2C19*16\u001b[0m \u001b[31mCYP2C19*17\u001b[0m \u001b[31mCYP2C19*18\u001b[0m \u001b[31mCYP2C19*19\u001b[0m \u001b[31mCYP2C19*20\u001b[0m \u001b[31mCYP2C19*21\u001b[0m \u001b[31mCYP2C19*22\u001b[0m \u001b[31mCYP2C19*23\u001b[0m \u001b[31mCYP2C19*24\u001b[0m \u001b[31mCYP2C19*25\u001b[0m \u001b[31mCYP2C19*26\u001b[0m \u001b[31mCYP2C19*27\u001b[0m \u001b[31mCYP2C19*28\u001b[0m \u001b[31mCYP2C19*29\u001b[0m \u001b[31mCYP2C19*30\u001b[0m\n", + "================= PMC4220464 =================\n", + "Experimental List:\n", + "\u001b[31mrs445925\u001b[0m \u001b[31mrs10455872\u001b[0m \u001b[31mrs2900478\u001b[0m \u001b[31mrs646776\u001b[0m\n", + "\n", + "Ground Truth List:\n", + "\u001b[31mrs113993960\u001b[0m \u001b[31mrs1057910\u001b[0m \u001b[31mrs4244285\u001b[0m \u001b[31mrs4986893\u001b[0m \u001b[31mrs28371607\u001b[0m \u001b[31mrs1801133\u001b[0m \u001b[31mrs1801131\u001b[0m \u001b[31mrs2236225\u001b[0m \u001b[31mrs9923231\u001b[0m \u001b[31mrs2735383\u001b[0m \u001b[31mrs3892097\u001b[0m \u001b[31mrs1065852\u001b[0m\n", + "================= PMC4730664 =================\n", + "Experimental List:\n", + "\u001b[31mCYP3A5*1, CYP3A5*3\u001b[0m \u001b[31mCYP3A5*1, CYP3A5*3\u001b[0m\n", + "\n", + "Ground Truth List:\n", + "\u001b[31mrs113993960\u001b[0m \u001b[31mCYP2C19*1\u001b[0m \u001b[31mCYP2C19*2\u001b[0m \u001b[31mCYP2C19*3\u001b[0m \u001b[31mCYP2C19*4\u001b[0m \u001b[31mCYP2C19*5\u001b[0m \u001b[31mCYP2C19*6\u001b[0m \u001b[31mCYP2C19*7\u001b[0m \u001b[31mCYP2C19*8\u001b[0m \u001b[31mCYP2C19*9\u001b[0m \u001b[31mCYP2C19*10\u001b[0m \u001b[31mCYP2C19*11\u001b[0m \u001b[31mCYP2C19*12\u001b[0m \u001b[31mCYP2C19*13\u001b[0m \u001b[31mCYP2C19*14\u001b[0m \u001b[31mCYP2C19*15\u001b[0m \u001b[31mCYP2C19*16\u001b[0m \u001b[31mCYP2C19*17\u001b[0m \u001b[31mCYP2C19*18\u001b[0m \u001b[31mCYP2C19*19\u001b[0m \u001b[31mCYP2C19*20\u001b[0m\n" + ] + } + ], + "source": [ + "for pmcid in extracted_variants.keys():\n", + " experimental = [x[\"variant_id\"] for x in extracted_variants[pmcid]]\n", + " ground_truth = [x[\"variant_id\"] for x in true_variants[pmcid]]\n", + " # print differences\n", + " compare_lists(ground_truth, experimental)" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['PMC6714673', 'PMC3553682', 'PMC4270923', 'PMC4557249', 'PMC4220464', 'PMC4730664'])\n", + "dict_keys(['PMC6714673', 'PMC3553682', 'PMC4270923', 'PMC4557249', 'PMC4220464', 'PMC4730664'])\n" + ] + } + ], + "source": [ + "print(extracted_variants.keys())\n", + "print(true_variants.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "list indices must be integers or slices, not str", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[268]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m [x[\u001b[33m'\u001b[39m\u001b[33mvariant_id\u001b[39m\u001b[33m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[43mground_truth\u001b[49m\u001b[43m[\u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m]\u001b[49m]\n", + "\u001b[31mTypeError\u001b[39m: list indices must be integers or slices, not str" + ] + } + ], + "source": [ + "[x['variant_id'] for x in ground_truth[pmcid]]" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [], + "source": [ + "true_variants = json.load(open(\"data/benchmark/true_variant_list.json\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'variant_id': 'rs2909451', 'gene': 'DPP4', 'allele': 'TT'},\n", + " {'variant_id': 'rs2285676', 'gene': 'KCNJ11', 'allele': 'CC'},\n", + " {'variant_id': 'rs163184', 'gene': 'KCNQ1', 'allele': 'GG'},\n", + " {'variant_id': 'rs7754840', 'gene': 'CDKAL1', 'allele': 'CG'},\n", + " {'variant_id': 'rs4664443', 'gene': 'DPP4', 'allele': 'GG'},\n", + " {'variant_id': 'rs1799853', 'gene': 'CYP2C9', 'allele': 'TT'},\n", + " {'variant_id': 'rs3765467', 'gene': 'GLP1R', 'allele': 'AG'},\n", + " {'variant_id': 'rs6923761', 'gene': 'GLP1R', 'allele': 'AA'}]" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "true_variants[\"PMC11730665\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-13 20:08:55.192\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-13 20:08:55.194\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-13 20:08:55.194\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.annotation_extraction.components\u001b[0m:\u001b[36mextract_variants_list\u001b[0m:\u001b[36m18\u001b[0m - \u001b[34m\u001b[1mModel: gpt-4o, Temperature: 0.1\u001b[0m\n", + "\u001b[32m2025-06-13 20:08:55.195\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.annotation_extraction.components\u001b[0m:\u001b[36mextract_variants_list\u001b[0m:\u001b[36m19\u001b[0m - \u001b[34m\u001b[1mPMCID: PMC11730665\u001b[0m\n" + ] + } + ], + "source": [ + "extracted_variants[\"PMC11730665\"] = extract_variants_list(pmcid=\"PMC11730665\", debug=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================= PMC11730665 =================\n", + "Experimental List:\n", + "\u001b[32mrs2909451\u001b[0m \u001b[32mrs2285676\u001b[0m \u001b[32mrs163184\u001b[0m \u001b[32mrs7754840\u001b[0m \u001b[32mrs4664443\u001b[0m \u001b[32mrs1799853\u001b[0m \u001b[32mrs3765467\u001b[0m \u001b[32mrs6923761\u001b[0m\n", + "\n", + "Ground Truth List:\n", + "\u001b[32mrs2909451\u001b[0m \u001b[32mrs4664443\u001b[0m \u001b[32mrs3765467\u001b[0m \u001b[32mrs6923761\u001b[0m \u001b[32mrs163184\u001b[0m \u001b[32mrs2285676\u001b[0m \u001b[32mrs7754840\u001b[0m \u001b[31mrs756992\u001b[0m \u001b[32mrs1799853\u001b[0m \u001b[31mrs1057910\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "(8, 0, 0, 2)" + ] + }, + "execution_count": 296, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experimental = [x[\"variant_id\"] for x in extracted_variants[\"PMC11730665\"]]\n", + "ground_truth = [x[\"variant_id\"] for x in true_variants[\"PMC11730665\"]]\n", + "# print differences\n", + "compare_lists(ground_truth, experimental, \"PMC11730665\")" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/pixi.lock b/pixi.lock index d3e417e..c3c527b 100644 --- a/pixi.lock +++ b/pixi.lock @@ -206,6 +206,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/statsmodels-0.14.4-py313h93df234_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/termcolor-3.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tiktoken-0.9.0-py313h9a4dfeb_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h5083fa2_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tokenizers-0.21.1-py313h9a4dfeb_0.conda @@ -1876,8 +1877,6 @@ packages: - python_abi 3.13.* *_cp313 constrains: - jinja2 >=3.0.0 - arch: arm64 - platform: osx license: BSD-3-Clause license_family: BSD size: 24757 @@ -2562,8 +2561,6 @@ packages: - python >=3.13,<3.14.0a0 - python >=3.13,<3.14.0a0 *_cp313 - python_abi 3.13.* *_cp313 - arch: arm64 - platform: osx license: Python-2.0 license_family: PSF size: 369054 @@ -2593,8 +2590,6 @@ packages: - python_abi 3.13.* *_cp313 constrains: - __osx >=11.0 - arch: arm64 - platform: osx license: MIT license_family: MIT size: 360004 @@ -2704,6 +2699,15 @@ packages: license_family: BSD size: 11901433 timestamp: 1727987142433 +- conda: https://conda.anaconda.org/conda-forge/noarch/termcolor-3.1.0-pyhd8ed1ab_0.conda + sha256: 04c7b49fbe6a2421ca9126f3f880877e995c457aed8afeb5ad18f03990ca8738 + md5: 951a99e94afe3d38bfdbd04902fe33b7 + depends: + - python >=3.9 + license: MIT + license_family: MIT + size: 13131 + timestamp: 1746039688416 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tiktoken-0.9.0-py313h9a4dfeb_0.conda sha256: 926d4a01195b3c5f907533583ea7a935b3355823292fdc955de497dee83e12d3 md5: 860cbdef367dc46f03660c739cdd6487 @@ -2717,8 +2721,6 @@ packages: - requests >=2.26.0 constrains: - __osx >=11.0 - arch: arm64 - platform: osx license: MIT license_family: MIT size: 827611 @@ -2744,8 +2746,6 @@ packages: - python_abi 3.13.* *_cp313 constrains: - __osx >=11.0 - arch: arm64 - platform: osx license: Apache-2.0 license_family: APACHE size: 2022803 diff --git a/pixi.toml b/pixi.toml index f505bbf..5f869f6 100644 --- a/pixi.toml +++ b/pixi.toml @@ -31,3 +31,4 @@ python-dotenv = ">=1.1.0,<2" black = ">=25.1.0,<26" datasets = ">=3.6.0,<4" litellm = ">=1.72.2,<2" +termcolor = ">=3.1.0,<4" diff --git a/src/annotation_extraction/components.py b/src/annotation_extraction/components.py index 9266075..248c561 100644 --- a/src/annotation_extraction/components.py +++ b/src/annotation_extraction/components.py @@ -15,7 +15,7 @@ Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.) """ -def extract_variants_list(article_text: str = None, pmcid: str = None) -> VariantList: +def extract_variants_list(article_text: str = None, pmcid: str = None, model: str = "gpt-4o", temperature: float = 0.1, debug: bool = False) -> VariantList: """Extract a list of variants from an article. Args: article_text: The text of the article. @@ -34,7 +34,11 @@ def extract_variants_list(article_text: str = None, pmcid: str = None) -> Varian if article_text is None: article_text = ArticleParser(pmcid=pmcid).get_article_text() - model = SimpleLLM() + if debug: + logger.debug(f"Model: {model}, Temperature: {temperature}") + logger.debug(f"PMCID: {pmcid}") + + model = SimpleLLM(model=model, temperature=temperature) prompt_generator = PromptGenerator(VARIANT_LIST_PROMPT, {"article_text": article_text}) prompt = prompt_generator.get_prompt() output = model.generate(prompt, response_format=VariantList) diff --git a/src/annotation_extraction/simple_inference.py b/src/annotation_extraction/simple_inference.py index 20d3860..b7ff70f 100644 --- a/src/annotation_extraction/simple_inference.py +++ b/src/annotation_extraction/simple_inference.py @@ -53,8 +53,8 @@ def generate(self, prompt: str, temperature: float = None, response_format: Base class Variant(BaseModel): """Variant.""" variant_id: str - gene: str - allele: str + gene: str | None = None + allele: str | None = None # TODO: Add field validation for gene and allele diff --git a/src/annotation_extraction/utils.py b/src/annotation_extraction/utils.py index 87f4002..8948ddb 100644 --- a/src/annotation_extraction/utils.py +++ b/src/annotation_extraction/utils.py @@ -1,7 +1,58 @@ import re - +from termcolor import colored # Example usage def extractVariants(text): # Note, seems to extract a ton of variants, not just the ones that are being studied variantRegex = r'\b([A-Z]+\d+[A-Z]*\*\d+|\brs\d+)\b' - return re.findall(variantRegex, text) or [] \ No newline at end of file + return re.findall(variantRegex, text) or [] + + +def compare_lists(experimental_list, ground_truth_list, pmcid): + """ + Compare experimental list with ground truth list and calculate performance metrics. + + Args: + experimental_list (list): List of predicted/experimental values + ground_truth_list (list): List of actual/ground truth values + pmcid (str): PMCID of the article + + Returns: + tuple: (true_positives, true_negatives, false_positives, false_negatives) + """ + # Convert lists to sets for efficient comparison + experimental_set = set(experimental_list) + ground_truth_set = set(ground_truth_list) + + # Calculate performance metrics + true_positives = len(experimental_set.intersection(ground_truth_set)) + false_positives = len(experimental_set - ground_truth_set) + false_negatives = len(ground_truth_set - experimental_set) + true_negatives = 0 # Not applicable in this context + + # Color-code the lists + colored_experimental = [] + colored_ground_truth = [] + + # Color experimental list + for item in experimental_list: + if item in ground_truth_set: + colored_experimental.append(colored(item, 'green')) + else: + colored_experimental.append(colored(item, 'red')) + + # Color ground truth list + for item in ground_truth_list: + if item in experimental_set: + colored_ground_truth.append(colored(item, 'green')) + else: + colored_ground_truth.append(colored(item, 'red')) + + # Print colored lists + print(f"================= {pmcid} =================") + print("Experimental List:") + print(' '.join(map(str, colored_experimental))) + print("\nGround Truth List:") + print(' '.join(map(str, colored_ground_truth))) + + # Return performance metrics + return true_positives, true_negatives, false_positives, false_negatives \ No newline at end of file From 660476aefa62a04a7db81b55c5c7bf0bfd99c34e Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Sat, 14 Jun 2025 14:23:06 -0700 Subject: [PATCH 09/28] feat: removed all claude code --- notebooks/test.ipynb | 24 +- src/annotation_extraction/__init__.py | 31 -- src/annotation_extraction/inference.py | 503 ++++-------------- src/annotation_extraction/models.py | 111 ---- src/annotation_extraction/pipeline.py | 2 +- src/annotation_extraction/prompts.py | 288 +--------- src/annotation_extraction/simple_inference.py | 115 ---- src/annotation_extraction/simple_prompts.py | 9 - src/annotation_extraction/testing.ipynb | 0 src/benchmark/README.md | 438 --------------- src/benchmark/__init__.py | 23 - src/benchmark/config.py | 107 ---- src/benchmark/data_loader.py | 192 ------- src/benchmark/evaluator.py | 217 -------- src/benchmark/metrics.py | 341 ------------ src/benchmark/models.py | 316 ----------- src/benchmark/pipeline.py | 408 -------------- src/benchmark/reporting.py | 393 -------------- 18 files changed, 121 insertions(+), 3397 deletions(-) delete mode 100644 src/annotation_extraction/models.py delete mode 100644 src/annotation_extraction/simple_inference.py delete mode 100644 src/annotation_extraction/simple_prompts.py delete mode 100644 src/annotation_extraction/testing.ipynb delete mode 100644 src/benchmark/README.md delete mode 100644 src/benchmark/__init__.py delete mode 100644 src/benchmark/config.py delete mode 100644 src/benchmark/data_loader.py delete mode 100644 src/benchmark/evaluator.py delete mode 100644 src/benchmark/metrics.py delete mode 100644 src/benchmark/models.py delete mode 100644 src/benchmark/pipeline.py delete mode 100644 src/benchmark/reporting.py diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb index c3e0d85..f067187 100644 --- a/notebooks/test.ipynb +++ b/notebooks/test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -48,12 +48,26 @@ }, { "cell_type": "code", - "execution_count": 283, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'ArticleInput' from 'src.annotation_extraction.models' (/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/models.py)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msimple_inference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SimpleLLM, PromptGenerator\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01marticle_parser\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ArticleParser\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m List\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/__init__.py:8\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[33;03mAnnotation extraction module for pharmacogenomic variant annotations.\u001b[39;00m\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m \u001b[33;03mThis module implements a multi-stage LLM pipeline for extracting structured\u001b[39;00m\n\u001b[32m 5\u001b[39m \u001b[33;03mpharmacogenomic variant annotations from biomedical articles.\u001b[39;00m\n\u001b[32m 6\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m AnnotationPipeline\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01minference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 10\u001b[39m RelevanceScreener,\n\u001b[32m 11\u001b[39m EntityExtractor,\n\u001b[32m (...)\u001b[39m\u001b[32m 14\u001b[39m QualityValidator\n\u001b[32m 15\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 17\u001b[39m ArticleInput,\n\u001b[32m 18\u001b[39m RelevanceResult,\n\u001b[32m (...)\u001b[39m\u001b[32m 22\u001b[39m ValidationResult\n\u001b[32m 23\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/pipeline.py:10\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mconcurrent\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfutures\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ThreadPoolExecutor, as_completed\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdataclasses\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m asdict\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 11\u001b[39m ArticleInput, RelevanceResult, ExtractedEntities, ClassificationResult,\n\u001b[32m 12\u001b[39m AnnotationRow, ValidationResult, AnnotationType\n\u001b[32m 13\u001b[39m )\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01minference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 15\u001b[39m LLMInterface, RelevanceScreener, EntityExtractor, AnnotationClassifier,\n\u001b[32m 16\u001b[39m RowGenerator, QualityValidator\n\u001b[32m 17\u001b[39m )\n\u001b[32m 18\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mprompts\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m PromptTemplates\n", + "\u001b[31mImportError\u001b[39m: cannot import name 'ArticleInput' from 'src.annotation_extraction.models' (/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/models.py)" + ] + } + ], "source": [ "from src.annotation_extraction.simple_inference import SimpleLLM, PromptGenerator\n", - "from src.annotation_extraction.models import ArticleParser\n", + "from src.annotation_extraction.article_parser import ArticleParser\n", "from typing import List" ] }, diff --git a/src/annotation_extraction/__init__.py b/src/annotation_extraction/__init__.py index de3b94f..bc091a9 100644 --- a/src/annotation_extraction/__init__.py +++ b/src/annotation_extraction/__init__.py @@ -5,34 +5,3 @@ pharmacogenomic variant annotations from biomedical articles. """ -from .pipeline import AnnotationPipeline -from .inference import ( - RelevanceScreener, - EntityExtractor, - AnnotationClassifier, - RowGenerator, - QualityValidator -) -from .models import ( - ArticleInput, - RelevanceResult, - ExtractedEntities, - ClassificationResult, - AnnotationRow, - ValidationResult -) - -__all__ = [ - 'AnnotationPipeline', - 'RelevanceScreener', - 'EntityExtractor', - 'AnnotationClassifier', - 'RowGenerator', - 'QualityValidator', - 'ArticleInput', - 'RelevanceResult', - 'ExtractedEntities', - 'ClassificationResult', - 'AnnotationRow', - 'ValidationResult' -] \ No newline at end of file diff --git a/src/annotation_extraction/inference.py b/src/annotation_extraction/inference.py index cb35484..b7ff70f 100644 --- a/src/annotation_extraction/inference.py +++ b/src/annotation_extraction/inference.py @@ -1,422 +1,115 @@ -""" -Individual stages of the annotation extraction pipeline. -""" - -import re -import time -import random -from typing import List, Optional, Dict, Any -from abc import ABC, abstractmethod -from litellm import completion +from typing import Dict from loguru import logger +import litellm +from .inference import LLMInterface +import re +import os +from dotenv import load_dotenv +from pydantic import BaseModel, field_validator +from typing import List +load_dotenv() -from .models import ( - ArticleInput, RelevanceResult, ExtractedEntities, ExtractedEntity, - ClassificationResult, AnnotationRow, ValidationResult, Relationship, - AnnotationType, PhenotypeCategory, Significance, Association, Direction -) -from .prompts import PromptTemplates - +class PromptGenerator: + def __init__(self, prompt_template: str, replacements: Dict[str, str]): + self.prompt_template = prompt_template + self.replacements = replacements + self.validate_replacements() + self.prompt = self.generate_prompt() + + def validate_replacements(self) -> bool: + """Validate that all replacement keys are present in the prompt template.""" + all_valid = True + for key in self.replacements: + if key not in self.prompt_template: + logger.warning(f"Replacement key {key} not found in prompt template.") + all_valid = False + return all_valid -class LLMInterface(ABC): - """Abstract interface for LLM interactions.""" + def generate_prompt(self) -> str: + return self.prompt_template.format(**self.replacements) + + def get_prompt(self) -> str: + return self.prompt + + def get_prompt_template(self) -> str: + return self.prompt_template + +class SimpleLLM(LLMInterface): + """Simple LLM interface that just returns as response to the prompt.""" + debug_mode = False - @abstractmethod - def generate(self, prompt: str, temperature: float = 0.1) -> str: - """Generate response from LLM.""" - pass - -class ModelConfig: def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): self.model = model self.temperature = temperature - -class RelevanceScreener: - """Stage 1: Determines if article contains relevant pharmacogenomic content.""" - - def __init__(self, llm: LLMInterface): - self.llm = llm - - def screen_article(self, article: ArticleInput) -> RelevanceResult: - """Screen article for pharmacogenomic relevance.""" - prompt = PromptTemplates.format_prompt( - PromptTemplates.RELEVANCE_SCREENING, - title=article.title, - article_text=article.article_text - ) - - response = self.llm.generate(prompt, temperature=0.1) - - # Parse response - lines = response.strip().split('\n') - first_line = lines[0].strip().upper() - is_relevant = first_line == "RELEVANT" - summary = None + if self.debug_mode: + litellm.set_verbose = True - if is_relevant and len(lines) > 1: - summary = '\n'.join(lines[1:]).strip() - - return RelevanceResult(is_relevant=is_relevant, summary=summary) + def generate(self, prompt: str, temperature: float = None, response_format: BaseModel = None) -> str: + temp = temperature if temperature is not None else self.temperature + response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], response_format=response_format, temperature=temp) + return response.choices[0].message.content +class Variant(BaseModel): + """Variant.""" + variant_id: str + gene: str | None = None + allele: str | None = None -class EntityExtractor: - """Stage 2: Extracts pharmacogenomic entities and relationships.""" - - def __init__(self, llm: LLMInterface): - self.llm = llm - - def extract_entities(self, article: ArticleInput) -> ExtractedEntities: - """Extract all relevant entities from article.""" - prompt = PromptTemplates.format_prompt( - PromptTemplates.ENTITY_EXTRACTION, - article_text=article.article_text - ) - - response = self.llm.generate(prompt, temperature=0.2) - - # Parse the structured response - entities = self._parse_entity_response(response) - - return entities - - def _parse_entity_response(self, response: str) -> ExtractedEntities: - """Parse LLM response into structured entities.""" - sections = { - 'GENETIC VARIANTS:': [], - 'DRUGS AND INTERVENTIONS:': [], - 'PHENOTYPES AND OUTCOMES:': [], - 'POPULATION INFORMATION:': [], - 'ASSOCIATIONS AND RELATIONSHIPS:': [] - } - - current_section = None - lines = response.split('\n') - - for line in lines: - line = line.strip() - if line in sections: - current_section = line - elif current_section and line.startswith('- '): - entity_text = line[2:].strip() - entity = ExtractedEntity( - entity_type=current_section.replace(':', '').strip(), - value=entity_text, - context="", - supporting_text=entity_text - ) - sections[current_section].append(entity) - - return ExtractedEntities( - genetic_variants=sections['GENETIC VARIANTS:'], - drugs=sections['DRUGS AND INTERVENTIONS:'], - phenotypes=sections['PHENOTYPES AND OUTCOMES:'], - population_info=sections['POPULATION INFORMATION:'], - associations=sections['ASSOCIATIONS AND RELATIONSHIPS:'] - ) + # TODO: Add field validation for gene and allele +class VariantList(BaseModel): + """List of variants.""" + variant_list: List[Variant] -class AnnotationClassifier: - """Stage 3: Classifies relationships into annotation types.""" - - def __init__(self, llm: LLMInterface): - self.llm = llm - - def classify_relationships(self, entities: ExtractedEntities, article: ArticleInput) -> List[ClassificationResult]: - """Classify each relationship into annotation types.""" - # Create relationships from associations - relationships = self._create_relationships(entities, article) - - if not relationships: - return [] - - # Format relationships for classification - relationships_text = self._format_relationships_for_prompt(relationships) - - prompt = PromptTemplates.format_prompt( - PromptTemplates.ANNOTATION_CLASSIFICATION, - relationships=relationships_text - ) - - response = self.llm.generate(prompt, temperature=0.1) - - # Parse classification results - return self._parse_classification_response(response, relationships) - - def _create_relationships(self, entities: ExtractedEntities, article: ArticleInput) -> List[Relationship]: - """Create relationships from extracted entities.""" - relationships = [] - - # Simple heuristic: pair variants with drugs and phenotypes - for variant in entities.genetic_variants: - # Extract gene from variant - gene = self._extract_gene_from_variant(variant.value) - - for drug in entities.drugs: - for phenotype in entities.phenotypes: - relationship = Relationship( - variant=variant.value, - gene=gene, - drug=drug.value, - phenotype=phenotype.value, - association_type="association", - evidence_text=f"{variant.supporting_text} {drug.supporting_text} {phenotype.supporting_text}", - statistical_measures=None - ) - relationships.append(relationship) - - return relationships - - def _extract_gene_from_variant(self, variant_text: str) -> str: - """Extract gene symbol from variant notation.""" - # Look for gene symbols (e.g., CYP2D6 from CYP2D6*4) - gene_pattern = r'([A-Z][A-Z0-9]+)(?:\*|\s|$)' - match = re.search(gene_pattern, variant_text) - return match.group(1) if match else "" - - def _format_relationships_for_prompt(self, relationships: List[Relationship]) -> str: - """Format relationships for classification prompt.""" - formatted = [] - for i, rel in enumerate(relationships): - formatted.append(f"{i+1}. {rel.variant} ({rel.gene}) - {rel.drug} - {rel.phenotype}") - return '\n'.join(formatted) - - def _parse_classification_response(self, response: str, relationships: List[Relationship]) -> List[ClassificationResult]: - """Parse classification response into results.""" - results = [] - lines = response.strip().split('\n') - - for line in lines: - if '|' in line: - parts = [p.strip() for p in line.split('|')] - if len(parts) >= 4: - try: - rel_id = int(parts[0]) - 1 - if 0 <= rel_id < len(relationships): - annotation_types = self._parse_annotation_types(parts[1]) - confidence = self._parse_confidence(parts[2]) - evidence = parts[3] - - result = ClassificationResult( - relationship=relationships[rel_id], - annotation_types=annotation_types, - confidence=confidence, - evidence_summary=evidence - ) - results.append(result) - except (ValueError, IndexError): - continue - - return results - - def _parse_annotation_types(self, type_text: str) -> List[AnnotationType]: - """Parse annotation types from text.""" - types = [] - type_text = type_text.lower() - - if 'functional' in type_text: - types.append(AnnotationType.FUNCTIONAL) - if 'drug' in type_text: - types.append(AnnotationType.DRUG) - if 'phenotype' in type_text: - types.append(AnnotationType.PHENOTYPE) - - return types or [AnnotationType.FUNCTIONAL] # Default to functional - - def _parse_confidence(self, confidence_text: str) -> float: - """Parse confidence level from text.""" - confidence_text = confidence_text.lower() - if 'high' in confidence_text: - return 0.9 - elif 'medium' in confidence_text: - return 0.7 - elif 'low' in confidence_text: - return 0.5 - else: - return 0.7 # Default +class StrEntry(BaseModel): + """String entry.""" + text: str + reason: str + quote: str +class EnumEntry(BaseModel): + """Enum entry.""" + options: List[str] + text: str + reason: str + quote: str -class RowGenerator: - """Stage 4: Generates schema-specific TSV rows.""" - - def __init__(self, llm: LLMInterface): - self.llm = llm - - def generate_annotation_rows(self, classifications: List[ClassificationResult], article: ArticleInput) -> List[AnnotationRow]: - """Generate annotation rows for all classifications.""" - all_rows = [] - - for classification in classifications: - for annotation_type in classification.annotation_types: - row = self._generate_single_row(classification, annotation_type, article) - if row: - all_rows.append(row) - - return all_rows - - def _generate_single_row(self, classification: ClassificationResult, - annotation_type: AnnotationType, article: ArticleInput) -> Optional[AnnotationRow]: - """Generate a single annotation row.""" - relationship = classification.relationship - - # Select appropriate prompt template - if annotation_type == AnnotationType.FUNCTIONAL: - template = PromptTemplates.FUNCTIONAL_ANNOTATION_GENERATION - elif annotation_type == AnnotationType.DRUG: - template = PromptTemplates.DRUG_ANNOTATION_GENERATION - elif annotation_type == AnnotationType.PHENOTYPE: - template = PromptTemplates.PHENOTYPE_ANNOTATION_GENERATION - else: - return None - - prompt = PromptTemplates.format_prompt( - template, - pmid=article.pmid, - relationship_description=f"{relationship.variant} - {relationship.drug} - {relationship.phenotype}", - supporting_sentence=relationship.evidence_text - ) - - response = self.llm.generate(prompt, temperature=0.1) - - # Parse the TSV row - return self._parse_tsv_response(response, annotation_type) - - def _parse_tsv_response(self, response: str, annotation_type: AnnotationType) -> Optional[AnnotationRow]: - """Parse LLM response into annotation row.""" - lines = response.strip().split('\n') - - # Find the TSV row (should be tab-separated) - for line in lines: - if '\t' in line and len(line.split('\t')) > 10: # Minimum field count - row_data = line.split('\t') - - # Generate unique ID if needed - if not row_data[0] or row_data[0] == '[Generate unique ID]': - row_data[0] = self._generate_unique_id() - - # Get field names based on annotation type - fields = self._get_field_names(annotation_type) - - return AnnotationRow( - annotation_type=annotation_type, - row_data=row_data, - fields=fields - ) - - return None - - def _generate_unique_id(self) -> str: - """Generate unique annotation ID.""" - timestamp = str(int(time.time())) - random_num = str(random.randint(1000, 9999)) - return timestamp + random_num - - def _get_field_names(self, annotation_type: AnnotationType) -> List[str]: - """Get field names for annotation type.""" - if annotation_type == AnnotationType.FUNCTIONAL: - return [ - "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", - "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", - "Specialty Population", "Assay type", "Metabolizer types", "isPlural", - "Is/Is Not associated", "Direction of effect", "Functional terms", - "Gene/gene product", "When treated with/exposed to/when assayed with", - "Multiple drugs And/or", "Cell type", "Comparison Allele(s) or Genotype(s)", - "Comparison Metabolizer types" - ] - elif annotation_type == AnnotationType.DRUG: - return [ - "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", - "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", - "Specialty Population", "Metabolizer types", "isPlural", - "Is/Is Not associated", "Direction of effect", "PD/PK terms", - "Multiple drugs And/or", "Population types", - "Population Phenotypes or diseases", "Multiple phenotypes or diseases And/or", - "Comparison Allele(s) or Genotype(s)", "Comparison Metabolizer types" - ] - elif annotation_type == AnnotationType.PHENOTYPE: - return [ - "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", - "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", - "Specialty Population", "Metabolizer types", "isPlural", - "Is/Is Not associated", "Direction of effect", "Side effect/efficacy/other", - "Phenotype", "Multiple phenotypes And/or", - "When treated with/exposed to/when assayed with", "Multiple drugs And/or", - "Population types", "Population Phenotypes or diseases", - "Multiple phenotypes or diseases And/or", - "Comparison Allele(s) or Genotype(s)", "Comparison Metabolizer types" - ] - else: - return [] + @field_validator('text') + @classmethod + def validate_text_in_options(cls, v: str, info) -> str: + if 'options' in info.data and v not in info.data['options']: + raise ValueError(f"Text {v} not in options {info.data['options']}") + return v -class QualityValidator: - """Stage 5: Validates generated annotation rows.""" - - def __init__(self, llm: LLMInterface): - self.llm = llm - - def validate_row(self, row: AnnotationRow, article: ArticleInput) -> ValidationResult: - """Validate a single annotation row.""" - # Basic validation first - basic_errors = self._basic_validation(row) - - if basic_errors: - return ValidationResult(is_valid=False, errors=basic_errors) - - # LLM-based validation - prompt = PromptTemplates.format_prompt( - PromptTemplates.QUALITY_VALIDATION, - annotation_row=row.to_tsv(), - relevant_text=article.article_text[:1000], # First 1000 chars - schema_fields=', '.join(row.fields) - ) - - response = self.llm.generate(prompt, temperature=0.1) - - return self._parse_validation_response(response, row) - - def _basic_validation(self, row: AnnotationRow) -> List[str]: - """Perform basic validation checks.""" - errors = [] - - # Check field count - expected_count = len(row.fields) - actual_count = len(row.row_data) - - if actual_count != expected_count: - errors.append(f"Field count mismatch: expected {expected_count}, got {actual_count}") - - # Check required fields are not empty - required_indices = [0, 1, 2, 4] # ID, Variant, Gene, PMID - for idx in required_indices: - if idx < len(row.row_data) and not row.row_data[idx].strip(): - errors.append(f"Required field '{row.fields[idx]}' is empty") - - # Validate controlled vocabulary fields - if len(row.row_data) > 5: # Phenotype Category - category = row.row_data[5] - valid_categories = ["Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] - if category and category not in valid_categories: - errors.append(f"Invalid Phenotype Category: {category}") - - if len(row.row_data) > 6: # Significance - significance = row.row_data[6] - valid_significance = ["yes", "no", "not stated"] - if significance and significance not in valid_significance: - errors.append(f"Invalid Significance: {significance}") - - return errors - - def _parse_validation_response(self, response: str, row: AnnotationRow) -> ValidationResult: - """Parse validation response from LLM.""" - lines = response.strip().split('\n') - - is_valid = any('VALID' in line.upper() for line in lines[:3]) - errors = [] - - # Extract error messages - for line in lines: - if line.startswith('- ') or line.startswith('* '): - errors.append(line[2:].strip()) - - return ValidationResult(is_valid=is_valid, errors=errors) \ No newline at end of file +class VarDrugResponse(BaseModel): + """Response from VarDrug.""" + variant_id: StrEntry + drug_id: StrEntry + effect: StrEntry + evidence: StrEntry + +class VarPhenoResponse(BaseModel): + """Response from VarPheno.""" + variant_id: StrEntry + phenotype: StrEntry + evidence: StrEntry + +class VarFAResponse(BaseModel): + """Response from VarFA.""" + variant_id: StrEntry + phenotype: StrEntry + evidence: StrEntry + +class VarDrugParser(LLMInterface): + """LLM interface that parses a VarDrug response into a dictionary.""" + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): + super().__init__(model, temperature) + if self.debug_mode: + litellm.set_verbose = True + + def generate(self, prompt: str, temperature: float = None) -> str: + temp = temperature if temperature is not None else self.temperature + response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], structured_output=True, temperature=temp) + return response.choices[0].message.content diff --git a/src/annotation_extraction/models.py b/src/annotation_extraction/models.py deleted file mode 100644 index 73c2453..0000000 --- a/src/annotation_extraction/models.py +++ /dev/null @@ -1,111 +0,0 @@ -""" -Data models for the annotation extraction pipeline. -""" - -from dataclasses import dataclass -from typing import List, Dict, Optional, Union -from enum import Enum -from loguru import logger -from pathlib import Path -class AnnotationType(Enum): - FUNCTIONAL = "functional" - DRUG = "drug" - PHENOTYPE = "phenotype" - - -class PhenotypeCategory(Enum): - METABOLISM_PK = "Metabolism/PK" - EFFICACY = "Efficacy" - TOXICITY = "Toxicity" - DOSAGE = "Dosage" - OTHER = "Other" - - -class Significance(Enum): - YES = "yes" - NO = "no" - NOT_STATED = "not stated" - - -class SpecialtyPopulation(Enum): - PEDIATRIC = "Pediatric" - GERIATRIC = "Geriatric" - - -class Direction(Enum): - INCREASED = "increased" - DECREASED = "decreased" - - -class Association(Enum): - IS = "Is" - IS_NOT = "Is Not" - ARE = "Are" - ARE_NOT = "Are Not" -@dataclass -class RelevanceResult: - """Result of relevance screening.""" - is_relevant: bool - summary: Optional[str] = None - confidence: Optional[float] = None - - -@dataclass -class ExtractedEntity: - """Single extracted entity.""" - entity_type: str - value: str - context: str - supporting_text: str - - -@dataclass -class ExtractedEntities: - """All extracted entities from an article.""" - genetic_variants: List[ExtractedEntity] - drugs: List[ExtractedEntity] - phenotypes: List[ExtractedEntity] - population_info: List[ExtractedEntity] - associations: List[ExtractedEntity] - - -@dataclass -class Relationship: - """Variant-outcome relationship.""" - variant: str - gene: str - drug: Optional[str] - phenotype: str - association_type: str - evidence_text: str - statistical_measures: Optional[Dict[str, Union[str, float]]] - - -@dataclass -class ClassificationResult: - """Result of annotation type classification.""" - relationship: Relationship - annotation_types: List[AnnotationType] - confidence: float - evidence_summary: str - - -@dataclass -class AnnotationRow: - """Generated annotation row for TSV output.""" - annotation_type: AnnotationType - row_data: List[str] - fields: List[str] - - def to_tsv(self) -> str: - """Convert to TSV format.""" - return "\t".join(self.row_data) - - -@dataclass -class ValidationResult: - """Result of quality validation.""" - is_valid: bool - errors: List[str] - corrected_row: Optional[AnnotationRow] = None - confidence_score: Optional[float] = None \ No newline at end of file diff --git a/src/annotation_extraction/pipeline.py b/src/annotation_extraction/pipeline.py index 23af21d..ea02c1c 100644 --- a/src/annotation_extraction/pipeline.py +++ b/src/annotation_extraction/pipeline.py @@ -8,7 +8,7 @@ from dataclasses import asdict from .models import ( - ArticleInput, RelevanceResult, ExtractedEntities, ClassificationResult, + RelevanceResult, ExtractedEntities, ClassificationResult, AnnotationRow, ValidationResult, AnnotationType ) from .inference import ( diff --git a/src/annotation_extraction/prompts.py b/src/annotation_extraction/prompts.py index 1885036..1a4db2d 100644 --- a/src/annotation_extraction/prompts.py +++ b/src/annotation_extraction/prompts.py @@ -1,291 +1,9 @@ -""" -Prompt templates for the LLM annotation pipeline. -""" - -from typing import Dict, Any - - class PromptTemplates: - """Container for all prompt templates used in the pipeline.""" - - VAR_DRUG_SUMMARY = """ - -You are extracting pharmacogenomic information from a biomedical article. Identify and extract the following entities and their relationships: - -Article: {article_text} - -Extract: -METHODS SECTION: -- Briefly summarize the methods and results of the study - -GENETIC VARIANTS (if present): -- SNP IDs (rs numbers) - - For each SNP, capture: - - Specific gene location - - Variant type (missense, synonymous, etc.) - - Potential functional impact on protein - - Observed genotype frequencies - - Explicitly link each genetic variant to: - - Specific drug response - - Changes in clinical outcomes - - Mechanism of pharmacological interaction -- Gene names and symbols -- Variant/haplotype names (e.g., CYP2D6*4, *1/*2) -- Allele designations -- Genotype combinations -- If/how the variants affected the outcome of the study - -DRUGS AND INTERVENTIONS: -- Drug names (generic and brand) -- Drug classes and categories -- Dosing information -- Treatment regimens - -PHENOTYPES AND OUTCOMES: -- Clinical phenotypes -- Adverse events and toxicities -- Efficacy measures -- Pharmacokinetic parameters (clearance, metabolism, etc.) -- Disease conditions -- If/how the phenotypes affected the outcome of the study - -POPULATION INFORMATION: -- Study population characteristics -- Demographics (age, ethnicity, gender) -- Sample sizes -- Inclusion/exclusion criteria - -ASSOCIATIONS AND RELATIONSHIPS: -- Statistical associations between variants and outcomes -- Odds ratios, p-values, confidence intervals -- Comparative statements (increased/decreased effects) -- Causal relationships - -Format your response as structured lists with clear entity types and associated context. - -""" + """Simple prompts for the LLM annotation pipeline.""" - RELEVANCE_SCREENING = """ + SUMMARIZE_PROMPT = """ You are a pharmacogenomics expert reviewing biomedical literature. Analyze this article and determine if it contains information about genetic variants and their associations with drug response, metabolism, toxicity, or disease phenotypes. Article Title: {title} Article Text: {article_text} - -Does this article contain: -1. Specific genetic variants (SNPs, haplotypes, alleles)? -2. Drug names or therapeutic interventions? -3. Clinical outcomes, drug responses, or phenotypic associations? -4. Population or patient cohort data? - -Respond with: RELEVANT or NOT_RELEVANT -If RELEVANT, provide a brief 2-3 sentence summary of the key pharmacogenomic findings.""" - - ENTITY_EXTRACTION = """ -You are extracting pharmacogenomic information from a biomedical article. Identify and extract the following entities and their relationships: - -Article: {article_text} - -Extract: - -GENETIC VARIANTS: -- SNP IDs (rs numbers) -- Gene names and symbols -- Variant/haplotype names (e.g., CYP2D6*4, *1/*2) -- Allele designations -- Genotype combinations - -DRUGS AND INTERVENTIONS: -- Drug names (generic and brand) -- Drug classes and categories -- Dosing information -- Treatment regimens - -PHENOTYPES AND OUTCOMES: -- Clinical phenotypes -- Adverse events and toxicities -- Efficacy measures -- Pharmacokinetic parameters (clearance, metabolism, etc.) -- Disease conditions - -POPULATION INFORMATION: -- Study population characteristics -- Demographics (age, ethnicity, gender) -- Sample sizes -- Inclusion/exclusion criteria - -ASSOCIATIONS AND RELATIONSHIPS: -- Statistical associations between variants and outcomes -- Odds ratios, p-values, confidence intervals -- Comparative statements (increased/decreased effects) -- Causal relationships - -Format your response as structured lists with clear entity types and associated context.""" - - ANNOTATION_CLASSIFICATION = """ -Based on the extracted entities and relationships, classify each variant-outcome association into annotation categories: - -Extracted Relationships: {relationships} - -Classification Rules: -- FUNCTIONAL ANNOTATION: In vitro studies, enzyme activity, protein expression, cellular assays, metabolism studies -- DRUG ANNOTATION: Clinical drug response, efficacy, dosing, therapeutic outcomes in patients -- PHENOTYPE ANNOTATION: Adverse events, toxicity, disease susceptibility, clinical phenotypes - -For each relationship, specify: -1. Annotation type(s) applicable -2. Confidence level (high/medium/low) -3. Key evidence supporting the classification - -Output format: -Relationship ID | Annotation Type(s) | Confidence | Evidence Summary""" - - FUNCTIONAL_ANNOTATION_GENERATION = """Generate a complete functional annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. - -Article PMID: {pmid} -Relationship: {relationship_description} -Relevant Text: {supporting_sentence} - -REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: - -Variant Annotation ID: [Generate unique ID: concatenate timestamp + random numbers] -Variant/Haplotypes: [Exact variant notation, e.g., "CYP2C19*1, CYP2C19*2" or "rs1234567"] -Gene: [Gene symbol only, e.g., "CYP2C19"] -Drug(s): [Generic drug name(s), e.g., "warfarin" or "clopidogrel, aspirin"] -PMID: [{pmid}] -Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] -Significance: [EXACTLY ONE: "yes", "no", "not stated"] -Notes: [Brief context or methodology, e.g., "in vitro expression study"] -Sentence: [Complete supporting sentence from article that describes the association] -Alleles: [Specific allele if different from Variant/Haplotypes, or empty] -Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or leave empty] -Assay type: [Laboratory method, e.g., "yeast microsomes", "human liver microsomes"] -Metabolizer types: [If applicable: "poor metabolizer", "intermediate metabolizer", etc.] -isPlural: [Leave empty - automatically determined] -Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] -Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty if no direction] -Functional terms: [Specific functional outcome, e.g., "activity of", "metabolism of", "clearance of"] -Gene/gene product: [Gene symbol if functional term relates to gene product] -When treated with/exposed to/when assayed with: [Drug context if applicable] -Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] -Cell type: [If in vitro study, specify cell type] -Comparison Allele(s) or Genotype(s): [Reference genotype for comparison] -Comparison Metabolizer types: [Reference metabolizer status if applicable] - -OUTPUT EXACTLY ONE TAB-SEPARATED ROW with all fields filled or empty as appropriate.""" - - DRUG_ANNOTATION_GENERATION = """Generate a complete drug annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. - -Article PMID: {pmid} -Relationship: {relationship_description} -Relevant Text: {supporting_sentence} - -REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: - -Variant Annotation ID: [Generate unique ID] -Variant/Haplotypes: [Exact variant notation] -Gene: [Gene symbol only] -Drug(s): [Generic drug name(s)] -PMID: [{pmid}] -Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] -Significance: [EXACTLY ONE: "yes", "no", "not stated"] -Notes: [Clinical context or study details] -Sentence: [Complete supporting sentence from article] -Alleles: [Specific allele if different from Variant/Haplotypes, or empty] -Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or empty] -Metabolizer types: [If applicable: metabolizer status] -isPlural: [Leave empty] -Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] -Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty] -PD/PK terms: [Pharmacokinetic/pharmacodynamic outcome, e.g., "concentrations of", "response to"] -Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] -Population types: [Ethnicity/ancestry if specified] -Population Phenotypes or diseases: [Disease with prefix: "Disease:", "Other:", "Side Effect:"] -Multiple phenotypes or diseases And/or: [EXACTLY ONE: "and", "or", or empty] -Comparison Allele(s) or Genotype(s): [Reference genotype] -Comparison Metabolizer types: [Reference metabolizer status] - -OUTPUT EXACTLY ONE TAB-SEPARATED ROW.""" - - PHENOTYPE_ANNOTATION_GENERATION = """Generate a complete phenotype annotation row for this pharmacogenomic relationship. You must fill ALL fields according to the exact schema below. - -Article PMID: {pmid} -Relationship: {relationship_description} -Relevant Text: {supporting_sentence} - -REQUIRED OUTPUT FORMAT - Tab-separated values in this exact order: - -Variant Annotation ID: [Generate unique ID] -Variant/Haplotypes: [Exact variant notation] -Gene: [Gene symbol only] -Drug(s): [Generic drug name(s)] -PMID: [{pmid}] -Phenotype Category: [EXACTLY ONE: "Metabolism/PK", "Efficacy", "Toxicity", "Dosage", "Other"] -Significance: [EXACTLY ONE: "yes", "no", "not stated"] -Notes: [Clinical context] -Sentence: [Complete supporting sentence from article] -Alleles: [Specific allele if different, or empty] -Specialty Population: [EXACTLY ONE: "Pediatric", "Geriatric", or empty] -Metabolizer types: [If applicable] -isPlural: [Leave empty] -Is/Is Not associated: [EXACTLY ONE: "Is", "Is Not", "Are", "Are Not"] -Direction of effect: [EXACTLY ONE: "increased", "decreased", or empty] -Side effect/efficacy/other: [Specific phenotype outcome with prefix: "Side Effect:", "Efficacy:", "Other:"] -Phenotype: [Primary phenotype, e.g., "Neutropenia", "Stevens-Johnson Syndrome"] -Multiple phenotypes And/or: [EXACTLY ONE: "and", "or", or empty] -When treated with/exposed to/when assayed with: [Drug context] -Multiple drugs And/or: [EXACTLY ONE: "and", "or", or empty] -Population types: [Ethnicity if specified] -Population Phenotypes or diseases: [Disease context with prefix] -Multiple phenotypes or diseases And/or: [EXACTLY ONE: "and", "or", or empty] -Comparison Allele(s) or Genotype(s): [Reference genotype] -Comparison Metabolizer types: [Reference metabolizer status] - -OUTPUT EXACTLY ONE TAB-SEPARATED ROW.""" - - QUALITY_VALIDATION = """Review this generated annotation row and validate it meets the schema requirements: - -Generated Row: {annotation_row} -Source Article Excerpt: {relevant_text} -Expected Schema: {schema_fields} - -Validation Checklist: -1. Are ALL required fields present in correct order? -2. Do controlled vocabulary fields use exact allowed values? -3. Is the variant notation standardized? -4. Are drug names generic/standard? -5. Does the sentence directly support the association? -6. Are comparison groups appropriate? -7. Is the statistical significance correctly interpreted? - -Output: -- VALID or INVALID -- List specific corrections needed -- Corrected row if invalid""" - - BATCH_PROCESSING = """ -This article contains multiple pharmacogenomic relationships. Generate separate annotation rows for each relationship. - -Article PMID: {pmid} -Article Text: {full_article} - -For each distinct variant-drug-outcome relationship found: -1. Determine annotation type (functional/drug/phenotype) -2. Generate complete TSV row following the appropriate schema -3. Ensure unique Variant Annotation IDs -4. Maintain consistency in variant nomenclature across rows - -Output format: -ANNOTATION_TYPE: [functional/drug/phenotype] -ROW: [complete tab-separated row] - -ANNOTATION_TYPE: [functional/drug/phenotype] -ROW: [complete tab-separated row] - -[Continue for all relationships found] - -Ensure no duplicate relationships and complete coverage of annotatable findings.""" - - @classmethod - def format_prompt(cls, template: str, **kwargs) -> str: - """Format a prompt template with provided parameters.""" - return template.format(**kwargs) \ No newline at end of file +""" diff --git a/src/annotation_extraction/simple_inference.py b/src/annotation_extraction/simple_inference.py deleted file mode 100644 index b7ff70f..0000000 --- a/src/annotation_extraction/simple_inference.py +++ /dev/null @@ -1,115 +0,0 @@ -from typing import Dict -from loguru import logger -import litellm -from .inference import LLMInterface -import re -import os -from dotenv import load_dotenv -from pydantic import BaseModel, field_validator -from typing import List -load_dotenv() - -class PromptGenerator: - def __init__(self, prompt_template: str, replacements: Dict[str, str]): - self.prompt_template = prompt_template - self.replacements = replacements - self.validate_replacements() - self.prompt = self.generate_prompt() - - def validate_replacements(self) -> bool: - """Validate that all replacement keys are present in the prompt template.""" - all_valid = True - for key in self.replacements: - if key not in self.prompt_template: - logger.warning(f"Replacement key {key} not found in prompt template.") - all_valid = False - return all_valid - - def generate_prompt(self) -> str: - return self.prompt_template.format(**self.replacements) - - def get_prompt(self) -> str: - return self.prompt - - def get_prompt_template(self) -> str: - return self.prompt_template - -class SimpleLLM(LLMInterface): - """Simple LLM interface that just returns as response to the prompt.""" - debug_mode = False - - def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): - self.model = model - self.temperature = temperature - - if self.debug_mode: - litellm.set_verbose = True - - def generate(self, prompt: str, temperature: float = None, response_format: BaseModel = None) -> str: - temp = temperature if temperature is not None else self.temperature - response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], response_format=response_format, temperature=temp) - return response.choices[0].message.content - -class Variant(BaseModel): - """Variant.""" - variant_id: str - gene: str | None = None - allele: str | None = None - - # TODO: Add field validation for gene and allele - -class VariantList(BaseModel): - """List of variants.""" - variant_list: List[Variant] - -class StrEntry(BaseModel): - """String entry.""" - text: str - reason: str - quote: str - -class EnumEntry(BaseModel): - """Enum entry.""" - options: List[str] - text: str - reason: str - quote: str - - @field_validator('text') - @classmethod - def validate_text_in_options(cls, v: str, info) -> str: - if 'options' in info.data and v not in info.data['options']: - raise ValueError(f"Text {v} not in options {info.data['options']}") - return v - - -class VarDrugResponse(BaseModel): - """Response from VarDrug.""" - variant_id: StrEntry - drug_id: StrEntry - effect: StrEntry - evidence: StrEntry - -class VarPhenoResponse(BaseModel): - """Response from VarPheno.""" - variant_id: StrEntry - phenotype: StrEntry - evidence: StrEntry - -class VarFAResponse(BaseModel): - """Response from VarFA.""" - variant_id: StrEntry - phenotype: StrEntry - evidence: StrEntry - -class VarDrugParser(LLMInterface): - """LLM interface that parses a VarDrug response into a dictionary.""" - def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): - super().__init__(model, temperature) - if self.debug_mode: - litellm.set_verbose = True - - def generate(self, prompt: str, temperature: float = None) -> str: - temp = temperature if temperature is not None else self.temperature - response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], structured_output=True, temperature=temp) - return response.choices[0].message.content diff --git a/src/annotation_extraction/simple_prompts.py b/src/annotation_extraction/simple_prompts.py deleted file mode 100644 index 7845789..0000000 --- a/src/annotation_extraction/simple_prompts.py +++ /dev/null @@ -1,9 +0,0 @@ -class SimplePrompts: - """Simple prompts for the LLM annotation pipeline.""" - - SUMMARIZE_PROMPT = """ -You are a pharmacogenomics expert reviewing biomedical literature. Analyze this article and determine if it contains information about genetic variants and their associations with drug response, metabolism, toxicity, or disease phenotypes. - -Article Title: {title} -Article Text: {article_text} -""" diff --git a/src/annotation_extraction/testing.ipynb b/src/annotation_extraction/testing.ipynb deleted file mode 100644 index e69de29..0000000 diff --git a/src/benchmark/README.md b/src/benchmark/README.md deleted file mode 100644 index 3f4130f..0000000 --- a/src/benchmark/README.md +++ /dev/null @@ -1,438 +0,0 @@ -# AutoGKB Benchmarking System - -A comprehensive framework for evaluating language model performance on pharmacogenomic knowledge extraction from scientific articles. - -## Overview - -This benchmarking system provides standardized evaluation of language models' ability to extract structured pharmacogenomic annotations from scientific literature. It implements the specifications outlined in the [Benchmarking System PRD](../../docs/benchmarking_system_prd.md). - -### Key Features - -- **Separated Response Generation and Evaluation**: Generate model responses to JSONL files, then evaluate separately -- **Response Caching**: Reuse generated responses for different evaluation metrics -- **Flexible Evaluation**: Evaluate any JSONL response file from external models -- **Multi-model Support**: OpenAI, Anthropic, and custom models -- **Comprehensive Metrics**: Field-specific scoring with weighted aggregation - -## Quick Start - -### 1. Separated Response Generation and Evaluation (Recommended) - -```python -from pathlib import Path -from src.benchmark import BenchmarkPipeline, BenchmarkConfig - -# Create configuration -config = BenchmarkConfig( - data_dir=Path("data"), - max_articles=50, - output_dir=Path("benchmark_results") -) - -# Initialize pipeline -pipeline = BenchmarkPipeline(config) - -# Step 1: Generate responses to JSONL files -model_configs = [ - {"name": "claude-3-sonnet", "model_name": "claude-3-sonnet-20240229"}, - {"name": "gpt-4", "model_name": "gpt-4"} -] - -response_files = pipeline.generate_responses(model_configs, split="test") -# Output: {'claude-3-sonnet': Path('benchmark_results/responses/claude-3-sonnet_test_123456.jsonl'), ...} - -# Step 2: Evaluate response files separately -results = {} -for model_name, response_file in response_files.items(): - result = pipeline.evaluate_responses_file(response_file) - results[model_name] = result -``` - -### 2. Combined Mode (Legacy) - -```python -# Traditional approach - generates and evaluates in one step -results = pipeline.run_benchmark(model_configs, split="test") -``` - -### 3. Evaluate External Response Files - -```python -# Evaluate any JSONL response file (from external models/systems) -result = pipeline.evaluate_responses_file(Path("path/to/external_responses.jsonl")) -print(f"Score: {result.aggregate_metrics['mean_overall_score']:.3f}") -``` - -### 4. Using the Example Script - -```bash -# Validate setup -python benchmark_example.py --validate - -# Run full benchmark (separated workflow) -python benchmark_example.py - -# Evaluate specific response file -python benchmark_example.py --evaluate benchmark_results/responses/gpt-4_test_123456.jsonl -``` - -### 5. Configuration File - -```python -from src.benchmark import BenchmarkConfig - -# Load from JSON -config = BenchmarkConfig.from_file(Path("config/benchmark_config.json")) - -# Or create programmatically -config = BenchmarkConfig( - data_dir=Path("data"), - output_dir=Path("benchmark_results"), - max_articles=100 -) -``` - -## Architecture - -### Core Components - -1. **BenchmarkDataLoader**: Loads and preprocesses articles and annotations -2. **EvaluationMetrics**: Implements scoring functions for different field types -3. **LanguageModelInterface**: Abstracts different LM APIs (OpenAI, Anthropic, etc.) -4. **BenchmarkEvaluator**: Orchestrates prediction generation and evaluation -5. **BenchmarkPipeline**: Main interface for running benchmarks -6. **BenchmarkReporter**: Generates reports and visualizations - -### Data Flow - -#### Separated Workflow (Recommended) -``` -Articles (Markdown) + Annotations (JSONL) - ↓ -BenchmarkDataLoader → BenchmarkSample objects - ↓ -LanguageModel → Response Generation → JSONL Files - ↓ -JSONL Files → Evaluation → Scores - ↓ -BenchmarkReporter → Reports -``` - -#### Combined Workflow (Legacy) -``` -Articles (Markdown) + Annotations (JSONL) - ↓ -BenchmarkDataLoader → BenchmarkSample objects - ↓ -LanguageModel → Predictions → EvaluationMetrics → Scores - ↓ -BenchmarkReporter → Reports -``` - -## Evaluation Tasks - -### Task 1: Entity Extraction -- **Goal**: Extract genes, drugs, variants from articles -- **Metrics**: Precision, Recall, F1-score per entity type -- **Fields**: `gene`, `drugs`, `variant_haplotypes`, `alleles` - -### Task 2: Relationship Classification -- **Goal**: Classify pharmacogenomic associations and effects -- **Metrics**: Classification accuracy, confusion matrices -- **Fields**: `significance`, `direction_of_effect`, `phenotype_category` - -### Task 3: Structured Sentence Generation -- **Goal**: Generate standardized sentences describing relationships -- **Metrics**: BLEU, ROUGE, semantic similarity -- **Fields**: `sentence` - -### Task 4: Population Context Extraction -- **Goal**: Identify specialty populations and disease contexts -- **Metrics**: Multi-label classification metrics -- **Fields**: `specialty_population`, `population_phenotypes_or_diseases` - -## Scoring System - -### Field Types and Scoring - -1. **Exact Match Fields** (Score: 1.0 or 0.0) - - Categorical fields requiring precise matches - - Examples: `significance`, `direction_of_effect` - -2. **Fuzzy Match Fields** (Score: 0.0-1.0) - - Entity names allowing for variations - - Uses sequence similarity with thresholds - - Examples: `gene`, `drugs`, `variant_haplotypes` - -3. **Semantic Match Fields** (Score: 0.0-1.0) - - Free text requiring semantic understanding - - Uses token-based similarity (extensible to embeddings) - - Examples: `sentence`, `notes` - -### Weighted Scoring - -Fields are weighted by importance: -- **High importance** (1.2): `significance`, `direction_of_effect` -- **Standard importance** (1.0): `gene`, `drugs`, `phenotype_category` -- **Lower importance** (0.6-0.8): Population context fields - -## Model Support - -### Supported Models - -1. **OpenAI Models**: GPT-4, GPT-3.5-turbo -2. **Anthropic Models**: Claude-3-Sonnet, Claude-3-Haiku -3. **Mock Model**: For testing and development - -### Adding New Models - -```python -from src.benchmark.models import LanguageModelInterface, ModelResponse - -class MyCustomModel(LanguageModelInterface): - def generate(self, prompt: str, **kwargs) -> ModelResponse: - # Implementation here - pass - - def is_available(self) -> bool: - # Check availability - pass -``` - -## Output and Reporting - -### Generated Files - -#### Response Generation Phase -1. **Response Files** (`responses/{model}_{split}_{timestamp}.jsonl`): Model predictions in JSONL format - ```json - {"pmcid": "PMC123", "model_name": "gpt-4", "timestamp": 1234567890, "gene": "CYP2C9", "drugs": "warfarin", ...} - ``` - -#### Evaluation Phase -2. **Summary Report** (`summary.html`): Comparative overview -3. **Detailed Reports** (`{model}_detailed.json`): Per-model analysis -4. **Performance Comparison** (`performance_comparison.json`): Side-by-side metrics -5. **Intermediate Results** (`intermediate/{model}_{split}_{timestamp}.json`): Evaluation results - -### JSONL Response File Format - -Each line contains a complete model prediction: -```json -{ - "pmcid": "PMC1234567", - "model_name": "gpt-4", - "timestamp": 1703123456.789, - "gene": "CYP2C9", - "drugs": "warfarin", - "significance": "yes", - "direction_of_effect": "decreased", - "phenotype_category": "Dosage", - "sentence": "CYP2C9 variants are associated with decreased warfarin dose requirements.", - "alleles": "*2/*3", - "comparison_alleles_or_genotypes": "*1/*1" -} -``` - -### Export Formats - -- **JSONL**: Sample-level predictions and scores (primary format) -- **CSV**: Tabular format for statistical analysis -- **JSON**: Structured data for programmatic access - -## Configuration Options - -### Key Parameters - -```python -@dataclass -class BenchmarkConfig: - # Data paths - data_dir: Path - articles_dir: Path - benchmark_dir: Path - - # Model settings - model_name: str - temperature: float = 0.0 - max_tokens: int = 4000 - - # Evaluation settings - batch_size: int = 10 - max_articles: Optional[int] = None - - # Field weights and matching strategies - field_weights: Dict[str, float] - exact_match_fields: List[str] - fuzzy_match_fields: List[str] - semantic_match_fields: List[str] -``` - -## Advanced Usage - -### Single Sample Analysis - -```python -# Analyze specific sample in detail -analysis = pipeline.analyze_sample( - pmcid="PMC1234567", - model_config={"name": "gpt-4", "model_name": "gpt-4"}, - split="test" -) - -print(f"Prediction: {analysis['prediction']}") -print(f"Ground truth: {analysis['ground_truth']}") -print(f"Scores: {analysis['scores']}") -``` - -### Custom Evaluation Metrics - -```python -from src.benchmark.metrics import EvaluationMetrics - -# Create custom metrics with different weights -custom_metrics = EvaluationMetrics(field_weights={ - "gene": 2.0, # Higher weight for gene extraction - "significance": 1.5, - "drugs": 1.0 -}) - -evaluator = BenchmarkEvaluator(model, custom_metrics, schema, categorical_fields) -``` - -### Batch Processing - -```python -# Process large datasets efficiently -config.batch_size = 50 # Larger batches -config.max_articles = 1000 # Limit for faster iteration - -results = pipeline.run_benchmark(model_configs, split="test") -``` - -## API Reference - -### Core Classes - -- **BenchmarkPipeline**: Main interface - - `generate_responses()`: Generate model responses to JSONL files - - `evaluate_responses_file()`: Evaluate JSONL response file and return scores - - `run_benchmark()`: Complete benchmark (generates + evaluates) - - `validate_setup()`: Check configuration - - `analyze_sample()`: Single sample analysis - -- **BenchmarkConfig**: Configuration management - - `from_file()`: Load from JSON - - `validate()`: Check settings - -- **EvaluationMetrics**: Scoring functions - - `evaluate_sample()`: Score single prediction - - `aggregate_results()`: Compute summary statistics - -### Model Interfaces - -- **LanguageModelInterface**: Abstract base class -- **OpenAIModel**: OpenAI API integration -- **AnthropicModel**: Anthropic API integration -- **MockModel**: Testing model - -## Best Practices - -### 1. Development Workflow -1. Start with `MockModel` for testing -2. Use small `max_articles` for development -3. Validate setup before full runs -4. Monitor API costs and rate limits - -### 2. Production Evaluation -1. Use full test set for final evaluation -2. Set `temperature=0.0` for reproducibility -3. Run multiple evaluation rounds for statistical significance -4. Save intermediate results for recovery - -### 3. Performance Optimization -1. Use separated workflow to cache responses -2. Use appropriate batch sizes -3. Parallelize evaluation when feasible -4. Monitor memory usage with large datasets - -## Separated vs Combined Workflows - -### Benefits of Separated Workflow - -1. **Cost Efficiency**: Avoid re-running expensive API calls during metric development -2. **Flexibility**: Evaluate external model responses from any source -3. **Debugging**: Inspect intermediate responses for issues -4. **Parallelization**: Generate responses and evaluation can be distributed -5. **Metric Development**: Iterate on evaluation metrics without regenerating responses -6. **Comparison**: Evaluate same responses with different metric configurations - -### When to Use Each Approach - -**Use Separated Workflow When:** -- Developing or iterating on evaluation metrics -- Working with expensive API models -- Need to evaluate external model outputs -- Want to cache responses for multiple evaluations -- Distributing workload across systems - -**Use Combined Workflow When:** -- Quick one-off evaluations -- Working with local/fast models -- Simple benchmarking scenarios - -## Troubleshooting - -### Common Issues - -1. **Data Loading Errors** - - Check file paths in configuration - - Verify JSONL format integrity - - Ensure article files exist - -2. **Model API Errors** - - Verify API keys are set - - Check rate limits and quotas - - Handle network timeouts - -3. **Evaluation Errors** - - Check JSON parsing in model responses - - Validate field schema consistency - - Handle missing or malformed predictions - -### Debug Mode - -```python -import logging -logging.basicConfig(level=logging.DEBUG) - -# Enable detailed logging -pipeline = BenchmarkPipeline(config) -``` - -## Contributing - -### Adding Features - -1. **New Models**: Implement `LanguageModelInterface` -2. **New Metrics**: Extend `EvaluationMetrics` -3. **New Reports**: Add to `BenchmarkReporter` - -### Testing - -```bash -# Run validation -python benchmark_example.py --validate - -# Test with mock model -python -c " -from src.benchmark import * -config = BenchmarkConfig(model_name='mock', max_articles=1) -pipeline = BenchmarkPipeline(config) -results = pipeline.run_benchmark([{'name': 'mock', 'model_name': 'mock'}]) -" -``` - -## License - -See [LICENSE](../../LICENSES/) for details. \ No newline at end of file diff --git a/src/benchmark/__init__.py b/src/benchmark/__init__.py deleted file mode 100644 index cb9c622..0000000 --- a/src/benchmark/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -AutoGKB Benchmarking System - -A comprehensive framework for evaluating language model performance on -pharmacogenomic knowledge extraction from scientific articles. -""" - -from .data_loader import BenchmarkDataLoader -from .evaluator import BenchmarkEvaluator -from .metrics import EvaluationMetrics -from .models import LanguageModelInterface -from .pipeline import BenchmarkPipeline -from .config import BenchmarkConfig - -__version__ = "1.0.0" -__all__ = [ - "BenchmarkDataLoader", - "BenchmarkEvaluator", - "EvaluationMetrics", - "LanguageModelInterface", - "BenchmarkPipeline", - "BenchmarkConfig" -] \ No newline at end of file diff --git a/src/benchmark/config.py b/src/benchmark/config.py deleted file mode 100644 index 5283e2a..0000000 --- a/src/benchmark/config.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -Configuration management for the benchmarking system. -""" - -from dataclasses import dataclass, field -from pathlib import Path -from typing import Dict, List, Optional, Any -import json - - -@dataclass -class BenchmarkConfig: - """Configuration class for benchmark system.""" - - # Data paths - data_dir: Path = field(default_factory=lambda: Path("data")) - articles_dir: Path = field(default_factory=lambda: Path("data/articles")) - benchmark_dir: Path = field(default_factory=lambda: Path("data/benchmark")) - - # Model configuration - model_name: str = "claude-3-sonnet" - model_params: Dict[str, Any] = field(default_factory=dict) - max_tokens: int = 4000 - temperature: float = 0.0 - - # Evaluation settings - batch_size: int = 10 - max_articles: Optional[int] = None - random_seed: int = 42 - - # Metrics configuration - exact_match_fields: List[str] = field(default_factory=lambda: [ - "gene", "drugs", "significance", "direction_of_effect", - "phenotype_category", "specialty_population" - ]) - - fuzzy_match_fields: List[str] = field(default_factory=lambda: [ - "variant_haplotypes", "alleles", "population_phenotypes_or_diseases" - ]) - - semantic_match_fields: List[str] = field(default_factory=lambda: [ - "sentence", "notes" - ]) - - # Scoring weights - field_weights: Dict[str, float] = field(default_factory=lambda: { - "gene": 1.0, - "drugs": 1.0, - "variant_haplotypes": 0.8, - "significance": 1.2, - "direction_of_effect": 1.2, - "phenotype_category": 1.0, - "sentence": 0.1, - "alleles": 0.8, - "specialty_population": 0.6, - "population_phenotypes_or_diseases": 0.6 - }) - - # Output configuration - output_dir: Path = field(default_factory=lambda: Path("results")) - save_predictions: bool = True - save_detailed_results: bool = True - - @classmethod - def from_file(cls, config_path: Path) -> "BenchmarkConfig": - """Load configuration from JSON file.""" - with open(config_path, 'r') as f: - config_dict = json.load(f) - - # Convert path strings to Path objects - for key in ['data_dir', 'articles_dir', 'benchmark_dir', 'output_dir']: - if key in config_dict: - config_dict[key] = Path(config_dict[key]) - - return cls(**config_dict) - - def to_file(self, config_path: Path) -> None: - """Save configuration to JSON file.""" - config_dict = {} - for key, value in self.__dict__.items(): - if isinstance(value, Path): - config_dict[key] = str(value) - else: - config_dict[key] = value - - with open(config_path, 'w') as f: - json.dump(config_dict, f, indent=2) - - def validate(self) -> None: - """Validate configuration parameters.""" - if not self.data_dir.exists(): - raise ValueError(f"Data directory does not exist: {self.data_dir}") - - if not self.articles_dir.exists(): - raise ValueError(f"Articles directory does not exist: {self.articles_dir}") - - if not self.benchmark_dir.exists(): - raise ValueError(f"Benchmark directory does not exist: {self.benchmark_dir}") - - if self.batch_size <= 0: - raise ValueError("Batch size must be positive") - - if self.temperature < 0 or self.temperature > 2: - raise ValueError("Temperature must be between 0 and 2") - - # Create output directory if it doesn't exist - self.output_dir.mkdir(parents=True, exist_ok=True) \ No newline at end of file diff --git a/src/benchmark/data_loader.py b/src/benchmark/data_loader.py deleted file mode 100644 index d5c22d6..0000000 --- a/src/benchmark/data_loader.py +++ /dev/null @@ -1,192 +0,0 @@ -""" -Data loading and preprocessing utilities for the benchmarking system. -""" - -import json -import re -from pathlib import Path -from typing import Dict, List, Optional, Tuple, Any -from dataclasses import dataclass - -from loguru import logger - - -@dataclass -class BenchmarkSample: - """A single benchmark sample containing article and annotation.""" - pmcid: str - article_title: str - article_content: str - annotation: Dict[str, Any] - article_path: str - - -class BenchmarkDataLoader: - """Loads and preprocesses benchmark data.""" - - def __init__(self, data_dir: Path, articles_dir: Path, benchmark_dir: Path): - self.data_dir = data_dir - self.articles_dir = articles_dir - self.benchmark_dir = benchmark_dir - self.column_mapping = self._load_column_mapping() - - def _load_column_mapping(self) -> Dict[str, str]: - """Load column mapping configuration.""" - mapping_path = self.benchmark_dir / "column_mapping.json" - if mapping_path.exists(): - with open(mapping_path, 'r') as f: - return json.load(f) - return {} - - def load_split(self, split: str = "train") -> List[BenchmarkSample]: - """Load a specific data split (train/val/test).""" - split_path = self.benchmark_dir / f"{split}.jsonl" - if not split_path.exists(): - raise FileNotFoundError(f"Split file not found: {split_path}") - - samples = [] - with open(split_path, 'r') as f: - for line_num, line in enumerate(f, 1): - try: - annotation = json.loads(line.strip()) - sample = self._create_sample(annotation) - if sample: - samples.append(sample) - except json.JSONDecodeError as e: - logger.warning(f"Invalid JSON at line {line_num}: {e}") - except Exception as e: - logger.warning(f"Error processing line {line_num}: {e}") - - logger.info(f"Loaded {len(samples)} samples from {split} split") - return samples - - def _create_sample(self, annotation: Dict[str, Any]) -> Optional[BenchmarkSample]: - """Create a benchmark sample from annotation data.""" - pmcid = annotation.get("pmcid") - if not pmcid: - logger.warning("Missing PMCID in annotation") - return None - - # Load article content - article_content = self._load_article_content(pmcid) - if not article_content: - logger.warning(f"Could not load article content for {pmcid}") - return None - - return BenchmarkSample( - pmcid=pmcid, - article_title=annotation.get("article_title", ""), - article_content=article_content, - annotation=annotation, - article_path=annotation.get("article_path", f"articles/{pmcid}.md") - ) - - def _load_article_content(self, pmcid: str) -> Optional[str]: - """Load article content from markdown file.""" - article_path = self.articles_dir / f"{pmcid}.md" - if not article_path.exists(): - return None - - try: - with open(article_path, 'r', encoding='utf-8') as f: - content = f.read() - return self._preprocess_article_content(content) - except Exception as e: - logger.warning(f"Error reading article {pmcid}: {e}") - return None - - def _preprocess_article_content(self, content: str) -> str: - """Preprocess article content for model consumption.""" - # Remove excessive whitespace - content = re.sub(r'\n\s*\n\s*\n', '\n\n', content) - - # Clean up markdown formatting - content = re.sub(r'^#{1,6}\s+', '', content, flags=re.MULTILINE) # Remove header markers - content = re.sub(r'\*\*(.*?)\*\*', r'\1', content) # Remove bold formatting - content = re.sub(r'\*(.*?)\*', r'\1', content) # Remove italic formatting - content = re.sub(r'\[(.*?)\]\(.*?\)', r'\1', content) # Remove links, keep text - - # Remove metadata section if present - if content.startswith('# '): - # Find the end of metadata section (usually after first ## heading) - lines = content.split('\n') - start_idx = 0 - for i, line in enumerate(lines): - if line.startswith('## Abstract') or line.startswith('## Introduction'): - start_idx = i - break - content = '\n'.join(lines[start_idx:]) - - return content.strip() - - def get_field_schema(self) -> Dict[str, str]: - """Get the schema of annotation fields.""" - return { - "pmcid": "PubMed Central ID", - "article_title": "Title of the article", - "variant_annotation_id": "Unique identifier for the annotation", - "variant_haplotypes": "Genetic variant or haplotype information", - "gene": "Gene symbol or name", - "drugs": "Drug name(s) involved in the association", - "pmid": "PubMed ID", - "phenotype_category": "Category of phenotype (Dosage, Efficacy, Metabolism/PK, etc.)", - "significance": "Statistical significance (yes/no/not stated)", - "notes": "Additional notes or context", - "sentence": "Structured sentence describing the association", - "alleles": "Specific alleles involved", - "specialty_population": "Special population (Pediatric, etc.)", - "metabolizer_types": "Metabolizer phenotype", - "is_plural": "Grammatical form (Is/Are)", - "is_is_not_associated": "Association type (Associated with/Not associated with)", - "direction_of_effect": "Direction of effect (increased/decreased/null)", - "pd_pk_terms": "Pharmacodynamic/pharmacokinetic terms", - "multiple_drugs_and_or": "Conjunction for multiple drugs (and/or)", - "population_types": "Population description", - "population_phenotypes_or_diseases": "Disease or phenotype context", - "multiple_phenotypes_or_diseases_and_or": "Conjunction for multiple conditions", - "comparison_alleles_or_genotypes": "Comparison group genotypes", - "comparison_metabolizer_types": "Comparison metabolizer types" - } - - def get_categorical_fields(self) -> Dict[str, List[str]]: - """Get possible values for categorical fields.""" - return { - "significance": ["yes", "no", "not stated"], - "phenotype_category": [ - "Dosage", "Efficacy", "Metabolism/PK", "Other", - "Toxicity", "Dosage, Efficacy", "Dosage, Metabolism/PK", - "Efficacy, Metabolism/PK", "Other, Metabolism/PK" - ], - "direction_of_effect": ["increased", "decreased", None], - "is_plural": ["Is", "Are"], - "is_is_not_associated": ["Associated with", "Not associated with"], - "multiple_drugs_and_or": ["and", "or"], - "multiple_phenotypes_or_diseases_and_or": ["and", "or"], - "specialty_population": ["Pediatric", None] - } - - def get_statistics(self, samples: List[BenchmarkSample]) -> Dict[str, Any]: - """Get statistics about the loaded samples.""" - if not samples: - return {} - - stats = { - "total_samples": len(samples), - "unique_pmcids": len(set(s.pmcid for s in samples)), - "avg_article_length": sum(len(s.article_content) for s in samples) / len(samples), - } - - # Field completion rates - field_counts = {} - for sample in samples: - for field, value in sample.annotation.items(): - if field not in field_counts: - field_counts[field] = 0 - if value is not None and value != "": - field_counts[field] += 1 - - stats["field_completion_rates"] = { - field: count / len(samples) for field, count in field_counts.items() - } - - return stats \ No newline at end of file diff --git a/src/benchmark/evaluator.py b/src/benchmark/evaluator.py deleted file mode 100644 index 92c3475..0000000 --- a/src/benchmark/evaluator.py +++ /dev/null @@ -1,217 +0,0 @@ -""" -Core evaluation logic for the benchmarking system. -""" - -import json -import re -from typing import Dict, List, Any, Optional -from dataclasses import dataclass -from loguru import logger - -from .data_loader import BenchmarkSample -from .metrics import EvaluationMetrics, SampleScore -from .models import LanguageModelInterface, PharmacogenomicPromptBuilder - - - -@dataclass -class EvaluationResult: - """Result of evaluating a language model on the benchmark.""" - model_name: str - sample_scores: List[SampleScore] - aggregate_metrics: Dict[str, Any] - error_analysis: Dict[str, Any] - total_samples: int - successful_predictions: int - failed_predictions: List[str] # PMCIDs of failed predictions - - -class BenchmarkEvaluator: - """Main evaluator for the benchmarking system.""" - - def __init__( - self, - model: LanguageModelInterface, - metrics: EvaluationMetrics, - schema: Dict[str, str], - categorical_fields: Dict[str, List[str]] - ): - self.model = model - self.metrics = metrics - self.schema = schema - self.categorical_fields = categorical_fields - self.prompt_builder = PharmacogenomicPromptBuilder(schema, categorical_fields) - - def evaluate(self, samples: List[BenchmarkSample]) -> EvaluationResult: - """Evaluate the model on a list of benchmark samples.""" - logger.info(f"Starting evaluation of {self.model.model_name} on {len(samples)} samples") - - sample_scores = [] - failed_predictions = [] - - for i, sample in enumerate(samples): - logger.info(f"Processing sample {i+1}/{len(samples)}: {sample.pmcid}") - - try: - # Generate prediction - prediction = self._generate_prediction(sample) - - if prediction is None: - failed_predictions.append(sample.pmcid) - continue - - # Evaluate prediction - sample_score = self.metrics.evaluate_sample( - prediction, sample.annotation, sample.pmcid - ) - sample_scores.append(sample_score) - - except Exception as e: - logger.error(f"Error processing sample {sample.pmcid}: {e}") - failed_predictions.append(sample.pmcid) - - # Aggregate results - aggregate_metrics = self.metrics.aggregate_results(sample_scores) - error_analysis = self.metrics.generate_error_analysis(sample_scores) - - logger.info(f"Evaluation completed: {len(sample_scores)} successful, {len(failed_predictions)} failed") - - return EvaluationResult( - model_name=self.model.model_name, - sample_scores=sample_scores, - aggregate_metrics=aggregate_metrics, - error_analysis=error_analysis, - total_samples=len(samples), - successful_predictions=len(sample_scores), - failed_predictions=failed_predictions - ) - - def _generate_prediction(self, sample: BenchmarkSample) -> Optional[Dict[str, Any]]: - """Generate prediction for a single sample.""" - try: - # Build prompt - prompt = self.prompt_builder.build_extraction_prompt(sample.article_content) - - # Generate response - response = self.model.generate(prompt) - - # Parse JSON response - prediction = self._parse_model_response(response.content) - - if prediction is None: - logger.warning(f"Failed to parse response for {sample.pmcid}") - return None - - # Validate and clean prediction - prediction = self._validate_prediction(prediction, sample) - - return prediction - - except Exception as e: - logger.error(f"Error generating prediction for {sample.pmcid}: {e}") - return None - - def _parse_model_response(self, response_content: str) -> Optional[Dict[str, Any]]: - """Parse JSON from model response.""" - # Try to extract JSON from response - json_patterns = [ - r'```json\s*(\{.*?\})\s*```', # JSON in code blocks - r'```\s*(\{.*?\})\s*```', # JSON in generic code blocks - r'(\{.*?\})', # Direct JSON - ] - - for pattern in json_patterns: - matches = re.findall(pattern, response_content, re.DOTALL) - for match in matches: - try: - return json.loads(match) - except json.JSONDecodeError: - continue - - # If no JSON found, try parsing the entire response - try: - return json.loads(response_content.strip()) - except json.JSONDecodeError: - logger.warning("Could not parse JSON from model response") - return None - - def _validate_prediction( - self, - prediction: Dict[str, Any], - sample: BenchmarkSample - ) -> Dict[str, Any]: - """Validate and clean prediction.""" - validated = {} - - # Ensure required fields are present - for field_name in self.schema.keys(): - if field_name in ['variant_annotation_id', 'pmid', 'article_path']: - continue # Skip metadata fields - - value = prediction.get(field_name) - - # Clean and validate field value - cleaned_value = self._clean_field_value(field_name, value) - validated[field_name] = cleaned_value - - # Add metadata from sample - validated['pmcid'] = sample.pmcid - validated['article_title'] = sample.article_title - - return validated - - def _clean_field_value(self, field_name: str, value: Any) -> Any: - """Clean and validate a field value.""" - if value is None or value == "": - return None - - # Convert to string and clean - if isinstance(value, (int, float)): - value = str(value) - elif not isinstance(value, str): - value = str(value) - - value = value.strip() - - # Handle categorical fields - if field_name in self.categorical_fields: - allowed_values = self.categorical_fields[field_name] - - # Try exact match first - for allowed in allowed_values: - if allowed is None and value.lower() in ['null', 'none', '']: - return None - elif allowed is not None and value.lower() == str(allowed).lower(): - return allowed - - # Try partial match for some fields - if field_name in ['significance']: - if 'yes' in value.lower() or 'significant' in value.lower(): - return 'yes' - elif 'no' in value.lower() or 'not significant' in value.lower(): - return 'no' - else: - return 'not stated' - - # If no match found, keep original value but log warning - logger.warning(f"Invalid value '{value}' for categorical field '{field_name}'") - - return value - - def evaluate_single(self, sample: BenchmarkSample) -> Optional[SampleScore]: - """Evaluate a single sample (useful for debugging).""" - try: - prediction = self._generate_prediction(sample) - if prediction is None: - return None - - return self.metrics.evaluate_sample( - prediction, sample.annotation, sample.pmcid - ) - except Exception as e: - logger.error(f"Error evaluating single sample {sample.pmcid}: {e}") - return None - - def get_prediction_only(self, sample: BenchmarkSample) -> Optional[Dict[str, Any]]: - """Get prediction without evaluation (useful for analysis).""" - return self._generate_prediction(sample) \ No newline at end of file diff --git a/src/benchmark/metrics.py b/src/benchmark/metrics.py deleted file mode 100644 index 89b0b38..0000000 --- a/src/benchmark/metrics.py +++ /dev/null @@ -1,341 +0,0 @@ -""" -Evaluation metrics and scoring functions for the benchmarking system. -""" - -import re -import json -from typing import Dict, List, Any, Tuple, Optional, Union -from dataclasses import dataclass -from difflib import SequenceMatcher -from loguru import logger - - - -@dataclass -class FieldScore: - """Score for a single field evaluation.""" - field_name: str - exact_match: bool - score: float - predicted: Any - expected: Any - error_type: Optional[str] = None - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary for JSON serialization.""" - return { - 'field_name': self.field_name, - 'exact_match': self.exact_match, - 'score': self.score, - 'predicted': self.predicted, - 'expected': self.expected, - 'error_type': self.error_type - } - - -@dataclass -class SampleScore: - """Score for a complete sample evaluation.""" - pmcid: str - field_scores: List[FieldScore] - overall_score: float - weighted_score: float - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary for JSON serialization.""" - return { - 'pmcid': self.pmcid, - 'field_scores': [fs.to_dict() for fs in self.field_scores], - 'overall_score': self.overall_score, - 'weighted_score': self.weighted_score - } - - -class EvaluationMetrics: - """Comprehensive evaluation metrics for pharmacogenomic extraction.""" - - def __init__(self, field_weights: Optional[Dict[str, float]] = None): - self.field_weights = field_weights or {} - self.default_weight = 1.0 - - def evaluate_sample( - self, - predicted: Dict[str, Any], - expected: Dict[str, Any], - pmcid: str - ) -> SampleScore: - """Evaluate a single sample prediction against ground truth.""" - field_scores = [] - - for field_name in expected.keys(): - if field_name in ['variant_annotation_id', 'pmid', 'article_path']: - continue # Skip metadata fields - - field_score = self._evaluate_field( - field_name, - predicted.get(field_name), - expected.get(field_name) - ) - field_scores.append(field_score) - - # Calculate overall scores - if field_scores: - overall_score = sum(fs.score for fs in field_scores) / len(field_scores) - weighted_score = self._calculate_weighted_score(field_scores) - else: - overall_score = 0.0 - weighted_score = 0.0 - - return SampleScore( - pmcid=pmcid, - field_scores=field_scores, - overall_score=overall_score, - weighted_score=weighted_score - ) - - def _evaluate_field( - self, - field_name: str, - predicted: Any, - expected: Any - ) -> FieldScore: - """Evaluate a single field.""" - # Handle None values - if expected is None and predicted is None: - return FieldScore(field_name, True, 1.0, predicted, expected) - - if expected is None or predicted is None: - return FieldScore( - field_name, - False, - 0.0, - predicted, - expected, - error_type="missing_value" - ) - - # Convert to strings for comparison - pred_str = str(predicted).strip() if predicted is not None else "" - exp_str = str(expected).strip() if expected is not None else "" - - # Exact match check - if pred_str.lower() == exp_str.lower(): - return FieldScore(field_name, True, 1.0, predicted, expected) - - # Field-specific scoring - score = self._calculate_field_score(field_name, pred_str, exp_str) - - return FieldScore( - field_name, - False, - score, - predicted, - expected, - error_type=self._classify_error(pred_str, exp_str) - ) - - def _calculate_field_score(self, field_name: str, predicted: str, expected: str) -> float: - """Calculate field-specific score.""" - # Categorical fields - require exact match - categorical_fields = { - "significance", "direction_of_effect", "phenotype_category", - "is_plural", "is_is_not_associated", "multiple_drugs_and_or", - "specialty_population" - } - - if field_name in categorical_fields: - return 1.0 if predicted.lower() == expected.lower() else 0.0 - - # Entity fields - use fuzzy matching - entity_fields = { - "gene", "drugs", "variant_haplotypes", "alleles", - "comparison_alleles_or_genotypes" - } - - if field_name in entity_fields: - return self._fuzzy_match_score(predicted, expected) - - # Text fields - use semantic similarity - text_fields = { - "sentence", "notes", "population_phenotypes_or_diseases", - "pd_pk_terms", "population_types" - } - - if field_name in text_fields: - return self._semantic_similarity_score(predicted, expected) - - # Default: fuzzy matching - return self._fuzzy_match_score(predicted, expected) - - def _fuzzy_match_score(self, predicted: str, expected: str) -> float: - """Calculate fuzzy match score using sequence similarity.""" - if not predicted and not expected: - return 1.0 - if not predicted or not expected: - return 0.0 - - # Normalize strings - pred_norm = self._normalize_entity(predicted) - exp_norm = self._normalize_entity(expected) - - # Calculate similarity - similarity = SequenceMatcher(None, pred_norm, exp_norm).ratio() - - # Apply threshold - partial credit for close matches - if similarity >= 0.9: - return 1.0 - elif similarity >= 0.7: - return 0.8 - elif similarity >= 0.5: - return 0.5 - else: - return 0.0 - - def _semantic_similarity_score(self, predicted: str, expected: str) -> float: - """Calculate semantic similarity score for text fields.""" - if not predicted and not expected: - return 1.0 - if not predicted or not expected: - return 0.0 - - # Simple token-based similarity (can be enhanced with embeddings) - pred_tokens = set(self._tokenize_text(predicted.lower())) - exp_tokens = set(self._tokenize_text(expected.lower())) - - if not pred_tokens and not exp_tokens: - return 1.0 - if not pred_tokens or not exp_tokens: - return 0.0 - - # Jaccard similarity - intersection = len(pred_tokens & exp_tokens) - union = len(pred_tokens | exp_tokens) - - return intersection / union if union > 0 else 0.0 - - def _normalize_entity(self, entity: str) -> str: - """Normalize entity names for comparison.""" - # Remove common prefixes/suffixes - entity = re.sub(r'^(rs|CYP|COMT)', '', entity, flags=re.IGNORECASE) - - # Remove special characters and normalize spacing - entity = re.sub(r'[^\w\s\*\+\-]', ' ', entity) - entity = re.sub(r'\s+', ' ', entity).strip() - - return entity.lower() - - def _tokenize_text(self, text: str) -> List[str]: - """Tokenize text for semantic comparison.""" - # Simple word tokenization - tokens = re.findall(r'\b\w+\b', text.lower()) - # Remove common stop words - stop_words = {'is', 'are', 'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'} - return [token for token in tokens if token not in stop_words and len(token) > 2] - - def _classify_error(self, predicted: str, expected: str) -> str: - """Classify the type of error.""" - if not predicted: - return "missing_prediction" - if not expected: - return "unexpected_prediction" - - # Length-based classification - if len(predicted) < len(expected) * 0.5: - return "incomplete_extraction" - if len(predicted) > len(expected) * 2: - return "over_extraction" - - return "content_mismatch" - - def _calculate_weighted_score(self, field_scores: List[FieldScore]) -> float: - """Calculate weighted average score.""" - total_weight = 0.0 - weighted_sum = 0.0 - - for field_score in field_scores: - weight = self.field_weights.get(field_score.field_name, self.default_weight) - weighted_sum += field_score.score * weight - total_weight += weight - - return weighted_sum / total_weight if total_weight > 0 else 0.0 - - def aggregate_results(self, sample_scores: List[SampleScore]) -> Dict[str, Any]: - """Aggregate results across all samples.""" - if not sample_scores: - return {} - - # Overall statistics - overall_scores = [s.overall_score for s in sample_scores] - weighted_scores = [s.weighted_score for s in sample_scores] - - results = { - "total_samples": len(sample_scores), - "mean_overall_score": sum(overall_scores) / len(overall_scores), - "mean_weighted_score": sum(weighted_scores) / len(weighted_scores), - "min_score": min(overall_scores), - "max_score": max(overall_scores) - } - - # Field-level statistics - field_stats = {} - all_field_scores = {} - - for sample_score in sample_scores: - for field_score in sample_score.field_scores: - field_name = field_score.field_name - if field_name not in all_field_scores: - all_field_scores[field_name] = [] - all_field_scores[field_name].append(field_score) - - for field_name, scores in all_field_scores.items(): - field_values = [s.score for s in scores] - exact_matches = sum(1 for s in scores if s.exact_match) - - field_stats[field_name] = { - "mean_score": sum(field_values) / len(field_values), - "exact_match_rate": exact_matches / len(scores), - "total_predictions": len(scores), - "error_types": self._count_error_types(scores) - } - - results["field_statistics"] = field_stats - - # Performance by score ranges - score_ranges = { - "excellent": sum(1 for s in overall_scores if s >= 0.9), - "good": sum(1 for s in overall_scores if 0.7 <= s < 0.9), - "fair": sum(1 for s in overall_scores if 0.5 <= s < 0.7), - "poor": sum(1 for s in overall_scores if s < 0.5) - } - results["score_distribution"] = score_ranges - - return results - - def _count_error_types(self, field_scores: List[FieldScore]) -> Dict[str, int]: - """Count error types for a field.""" - error_counts = {} - for score in field_scores: - if score.error_type: - error_counts[score.error_type] = error_counts.get(score.error_type, 0) + 1 - return error_counts - - def generate_error_analysis(self, sample_scores: List[SampleScore]) -> Dict[str, Any]: - """Generate detailed error analysis.""" - error_analysis = { - "common_errors": {}, - "field_specific_issues": {}, - "difficult_samples": [] - } - - # Find samples with low scores - low_score_samples = [s for s in sample_scores if s.overall_score < 0.5] - error_analysis["difficult_samples"] = [ - { - "pmcid": s.pmcid, - "overall_score": s.overall_score, - "main_issues": [fs.field_name for fs in s.field_scores if fs.score < 0.3] - } - for s in low_score_samples[:10] # Top 10 most difficult - ] - - return error_analysis \ No newline at end of file diff --git a/src/benchmark/models.py b/src/benchmark/models.py deleted file mode 100644 index 0340e96..0000000 --- a/src/benchmark/models.py +++ /dev/null @@ -1,316 +0,0 @@ -""" -Language model interface and prompting system for the benchmarking framework. -""" - -import json -import time -from abc import ABC, abstractmethod -from typing import Dict, Any, List, Optional -from dataclasses import dataclass -from loguru import logger - - - -@dataclass -class ModelResponse: - """Response from a language model.""" - content: str - model_name: str - timestamp: float - metadata: Dict[str, Any] - raw_response: Optional[Any] = None - - -class LanguageModelInterface(ABC): - """Abstract interface for language models.""" - - def __init__(self, model_name: str, **kwargs): - self.model_name = model_name - self.config = kwargs - - @abstractmethod - def generate(self, prompt: str, **kwargs) -> ModelResponse: - """Generate response from the model.""" - pass - - @abstractmethod - def is_available(self) -> bool: - """Check if the model is available.""" - pass - - -class OpenAIModel(LanguageModelInterface): - """OpenAI GPT model interface.""" - - def __init__(self, model_name: str = "gpt-4", api_key: Optional[str] = None, **kwargs): - super().__init__(model_name, **kwargs) - self.api_key = api_key - try: - import openai - self.client = openai.OpenAI(api_key=api_key) - except ImportError: - raise ImportError("openai package required for OpenAI models") - - def generate(self, prompt: str, **kwargs) -> ModelResponse: - """Generate response using OpenAI API.""" - try: - response = self.client.chat.completions.create( - model=self.model_name, - messages=[{"role": "user", "content": prompt}], - temperature=kwargs.get("temperature", 0.0), - max_tokens=kwargs.get("max_tokens", 4000), - **{k: v for k, v in kwargs.items() if k not in ["temperature", "max_tokens"]} - ) - - return ModelResponse( - content=response.choices[0].message.content, - model_name=self.model_name, - timestamp=time.time(), - metadata={ - "usage": response.usage.dict() if response.usage else {}, - "finish_reason": response.choices[0].finish_reason - }, - raw_response=response - ) - except Exception as e: - logger.error(f"Error generating response from {self.model_name}: {e}") - raise - - def is_available(self) -> bool: - """Check if OpenAI API is available.""" - if not self.api_key: - return False - try: - self.client.models.list() - return True - except Exception: - return False - - -class AnthropicModel(LanguageModelInterface): - """Anthropic Claude model interface.""" - - def __init__(self, model_name: str = "claude-3-sonnet-20240229", api_key: Optional[str] = None, **kwargs): - super().__init__(model_name, **kwargs) - self.api_key = api_key - try: - import anthropic - self.client = anthropic.Anthropic(api_key=api_key) - except ImportError: - raise ImportError("anthropic package required for Anthropic models") - - def generate(self, prompt: str, **kwargs) -> ModelResponse: - """Generate response using Anthropic API.""" - try: - response = self.client.messages.create( - model=self.model_name, - messages=[{"role": "user", "content": prompt}], - temperature=kwargs.get("temperature", 0.0), - max_tokens=kwargs.get("max_tokens", 4000), - **{k: v for k, v in kwargs.items() if k not in ["temperature", "max_tokens"]} - ) - - return ModelResponse( - content=response.content[0].text, - model_name=self.model_name, - timestamp=time.time(), - metadata={ - "usage": { - "input_tokens": response.usage.input_tokens, - "output_tokens": response.usage.output_tokens - }, - "stop_reason": response.stop_reason - }, - raw_response=response - ) - except Exception as e: - logger.error(f"Error generating response from {self.model_name}: {e}") - raise - - def is_available(self) -> bool: - """Check if Anthropic API is available.""" - if not self.api_key: - return False - try: - # Simple test call - self.client.messages.create( - model=self.model_name, - messages=[{"role": "user", "content": "test"}], - max_tokens=1 - ) - return True - except Exception: - return False - - -class MockModel(LanguageModelInterface): - """Mock model for testing purposes.""" - - def __init__(self, model_name: str = "mock-model", **kwargs): - super().__init__(model_name, **kwargs) - - def generate(self, prompt: str, **kwargs) -> ModelResponse: - """Generate mock response.""" - # Simple mock response that tries to extract basic info - mock_content = ''' - { - "pmcid": "PMC1234567", - "article_title": "Mock Article", - "gene": "CYP2C9", - "drugs": "warfarin", - "significance": "yes", - "direction_of_effect": "decreased", - "phenotype_category": "Dosage", - "sentence": "Mock sentence describing the association.", - "alleles": "CT", - "comparison_alleles_or_genotypes": "CC" - } - ''' - - return ModelResponse( - content=mock_content.strip(), - model_name=self.model_name, - timestamp=time.time(), - metadata={"mock": True}, - raw_response=None - ) - - def is_available(self) -> bool: - """Mock model is always available.""" - return True - - -class PharmacogenomicPromptBuilder: - """Builds prompts for pharmacogenomic extraction tasks.""" - - def __init__(self, schema: Dict[str, str], categorical_fields: Dict[str, List[str]]): - self.schema = schema - self.categorical_fields = categorical_fields - - def build_extraction_prompt(self, article_content: str) -> str: - """Build prompt for extracting pharmacogenomic annotations.""" - schema_description = self._format_schema() - examples = self._get_examples() - - prompt = f""" -You are a pharmacogenomics expert tasked with extracting structured information from scientific articles. - -Your task is to analyze the following article and extract pharmacogenomic associations in the specified JSON format. - -## Schema Description: -{schema_description} - -## Important Guidelines: -1. Extract only information that is explicitly stated in the article -2. Use exact values from the categorical fields when applicable -3. For significance, use "yes" if statistically significant, "no" if explicitly not significant, "not stated" if unclear -4. For direction_of_effect, use "increased", "decreased", or null -5. Generate a structured sentence that captures the main finding -6. If multiple associations are described, focus on the primary/most significant one - -## Examples: -{examples} - -## Article to Analyze: -{article_content} - -## Your Response: -Please provide the extracted information as a valid JSON object: -""" - - return prompt - - def _format_schema(self) -> str: - """Format schema description for the prompt.""" - schema_lines = [] - for field, description in self.schema.items(): - if field in ['variant_annotation_id', 'pmid', 'article_path']: - continue # Skip metadata fields - - line = f"- {field}: {description}" - - # Add categorical options - if field in self.categorical_fields: - options = self.categorical_fields[field] - if None in options: - options = [str(opt) if opt is not None else "null" for opt in options] - line += f" (Options: {', '.join(str(opt) for opt in options)})" - - schema_lines.append(line) - - return '\n'.join(schema_lines) - - def _get_examples(self) -> str: - """Get example extractions for few-shot prompting.""" - examples = [ - { - "context": "Study found that patients with CYP2C9*3 variant required 25% lower warfarin doses compared to wild-type.", - "output": { - "gene": "CYP2C9", - "drugs": "warfarin", - "variant_haplotypes": "CYP2C9*3", - "significance": "yes", - "direction_of_effect": "decreased", - "phenotype_category": "Dosage", - "sentence": "CYP2C9*3 is associated with decreased dose of warfarin compared to wild-type.", - "alleles": "*3", - "comparison_alleles_or_genotypes": "wild-type" - } - }, - { - "context": "No significant association was found between rs123456 and response to drug X in this population.", - "output": { - "variant_haplotypes": "rs123456", - "drugs": "drug X", - "significance": "no", - "direction_of_effect": None, - "phenotype_category": "Efficacy", - "sentence": "rs123456 is not associated with response to drug X.", - "is_is_not_associated": "Not associated with" - } - } - ] - - example_text = "" - for i, example in enumerate(examples, 1): - example_text += f"\nExample {i}:\n" - example_text += f"Context: {example['context']}\n" - example_text += f"Output: {json.dumps(example['output'], indent=2)}\n" - - return example_text - - def build_validation_prompt(self, extraction: Dict[str, Any], article_content: str) -> str: - """Build prompt for validating extractions.""" - prompt = f""" -Please validate the following pharmacogenomic extraction against the source article. - -## Extraction to Validate: -{json.dumps(extraction, indent=2)} - -## Source Article: -{article_content[:2000]}... - -## Validation Task: -1. Check if the extracted information is accurate and supported by the article -2. Identify any missing key information -3. Flag any potential errors or inconsistencies - -Please provide a validation report in JSON format with: -- "is_valid": boolean -- "confidence_score": 0-1 -- "issues": list of identified problems -- "suggestions": list of improvements -""" - return prompt - - -def create_model(model_name: str, **kwargs) -> LanguageModelInterface: - """Factory function to create language model instances.""" - if model_name.startswith("gpt"): - return OpenAIModel(model_name, **kwargs) - elif model_name.startswith("claude"): - return AnthropicModel(model_name, **kwargs) - elif model_name == "mock": - return MockModel(model_name, **kwargs) - else: - raise ValueError(f"Unsupported model: {model_name}") \ No newline at end of file diff --git a/src/benchmark/pipeline.py b/src/benchmark/pipeline.py deleted file mode 100644 index 8febfd5..0000000 --- a/src/benchmark/pipeline.py +++ /dev/null @@ -1,408 +0,0 @@ -""" -Main benchmark pipeline orchestrating the entire evaluation process. -""" - -import json -import time -from pathlib import Path -from typing import Dict, List, Any, Optional -from loguru import logger - - -from .config import BenchmarkConfig -from .data_loader import BenchmarkDataLoader, BenchmarkSample -from .metrics import EvaluationMetrics -from .models import create_model, LanguageModelInterface -from .evaluator import BenchmarkEvaluator, EvaluationResult -from .reporting import BenchmarkReporter - -class BenchmarkPipeline: - """Main pipeline for running benchmarks.""" - - def __init__(self, config: BenchmarkConfig): - self.config = config - config.validate() - - # Initialize components - self.data_loader = BenchmarkDataLoader( - config.data_dir, - config.articles_dir, - config.benchmark_dir - ) - - self.metrics = EvaluationMetrics(config.field_weights) - self.reporter = BenchmarkReporter(config.output_dir) - - # Get schema information - self.schema = self.data_loader.get_field_schema() - self.categorical_fields = self.data_loader.get_categorical_fields() - - def generate_responses( - self, - model_configs: List[Dict[str, Any]], - split: str = "test" - ) -> Dict[str, Path]: - """Generate model responses and save to JSONL files.""" - logger.info(f"Generating responses on {split} split with {len(model_configs)} models") - - # Load data - samples = self.data_loader.load_split(split) - if self.config.max_articles: - samples = samples[:self.config.max_articles] - - logger.info(f"Loaded {len(samples)} samples for response generation") - - # Generate responses for each model - response_files = {} - for model_config in model_configs: - model_name = model_config.get("name", "unknown") - logger.info(f"Generating responses for model: {model_name}") - - try: - # Create model - model = create_model(**model_config) - - # Check availability - if not model.is_available(): - logger.warning(f"Model {model_name} is not available, skipping") - continue - - # Generate responses file - response_file = self._generate_model_responses(model, samples, split) - response_files[model_name] = response_file - - except Exception as e: - logger.error(f"Error generating responses for model {model_name}: {e}") - continue - - logger.info(f"Response generation completed for {len(response_files)} models") - return response_files - - def evaluate_responses_file(self, response_file: Path) -> EvaluationResult: - """Evaluate a JSONL file of model responses and return score.""" - logger.info(f"Evaluating responses from {response_file}") - - # Load responses from file - responses = self._load_responses_from_file(response_file) - - if not responses: - raise ValueError(f"No valid responses found in {response_file}") - - # Extract model name from first response - model_name = responses[0].get("model_name", "unknown") - - # Load ground truth data - split = self._extract_split_from_filename(response_file) - samples_dict = {s.pmcid: s for s in self.data_loader.load_split(split)} - - # Evaluate each response - sample_scores = [] - failed_predictions = [] - - for response in responses: - pmcid = response.get("pmcid") - if pmcid not in samples_dict: - logger.warning(f"No ground truth found for PMCID: {pmcid}") - failed_predictions.append(pmcid) - continue - - try: - ground_truth = samples_dict[pmcid].annotation - prediction = {k: v for k, v in response.items() - if k not in ["model_name", "timestamp", "pmcid"]} - - sample_score = self.metrics.evaluate_sample(prediction, ground_truth, pmcid) - sample_scores.append(sample_score) - - except Exception as e: - logger.error(f"Error evaluating response for {pmcid}: {e}") - failed_predictions.append(pmcid) - - # Aggregate results - aggregate_metrics = self.metrics.aggregate_results(sample_scores) - error_analysis = self.metrics.generate_error_analysis(sample_scores) - - logger.info(f"Evaluation completed: {len(sample_scores)} successful, {len(failed_predictions)} failed") - - return EvaluationResult( - model_name=model_name, - sample_scores=sample_scores, - aggregate_metrics=aggregate_metrics, - error_analysis=error_analysis, - total_samples=len(responses), - successful_predictions=len(sample_scores), - failed_predictions=failed_predictions - ) - - def run_benchmark( - self, - model_configs: List[Dict[str, Any]], - split: str = "test" - ) -> Dict[str, EvaluationResult]: - """Run benchmark on multiple models (legacy method - generates and evaluates in one step).""" - logger.info(f"Starting benchmark on {split} split with {len(model_configs)} models") - - # Generate responses - response_files = self.generate_responses(model_configs, split) - - # Evaluate each response file - results = {} - for model_name, response_file in response_files.items(): - try: - result = self.evaluate_responses_file(response_file) - results[model_name] = result - - # Save intermediate results - self._save_intermediate_result(result, split) - - except Exception as e: - logger.error(f"Error evaluating model {model_name}: {e}") - continue - - # Generate comparative report - if results: - self.reporter.generate_comparative_report(results, split) - - logger.info(f"Benchmark completed for {len(results)} models") - return results - - def run_single_model( - self, - model_config: Dict[str, Any], - split: str = "test" - ) -> EvaluationResult: - """Run benchmark on a single model.""" - results = self.run_benchmark([model_config], split) - model_name = model_config.get("name", "unknown") - - if model_name not in results: - raise RuntimeError(f"Failed to evaluate model {model_name}") - - return results[model_name] - - def analyze_sample( - self, - pmcid: str, - model_config: Dict[str, Any], - split: str = "test" - ) -> Dict[str, Any]: - """Analyze a specific sample in detail.""" - logger.info(f"Analyzing sample {pmcid}") - - # Load sample - samples = self.data_loader.load_split(split) - sample = next((s for s in samples if s.pmcid == pmcid), None) - - if sample is None: - raise ValueError(f"Sample {pmcid} not found in {split} split") - - # Create model and evaluator - model = create_model(**model_config) - evaluator = BenchmarkEvaluator( - model=model, - metrics=self.metrics, - schema=self.schema, - categorical_fields=self.categorical_fields - ) - - # Get prediction and evaluation - prediction = evaluator.get_prediction_only(sample) - sample_score = evaluator.evaluate_single(sample) - - analysis = { - "pmcid": pmcid, - "article_title": sample.article_title, - "model": model_config.get("name", "unknown"), - "prediction": prediction, - "ground_truth": sample.annotation, - "scores": sample_score.to_dict() if sample_score else None, - "article_content_preview": sample.article_content[:1000] + "..." - } - - # Save detailed analysis - self.reporter.save_sample_analysis(analysis) - - return analysis - - def validate_setup(self) -> Dict[str, Any]: - """Validate the benchmark setup.""" - validation_report = { - "config_valid": True, - "data_available": True, - "models_accessible": {}, - "issues": [] - } - - try: - # Check data availability - train_samples = self.data_loader.load_split("train") - val_samples = self.data_loader.load_split("val") - test_samples = self.data_loader.load_split("test") - - validation_report["data_statistics"] = { - "train_samples": len(train_samples), - "val_samples": len(val_samples), - "test_samples": len(test_samples) - } - - if not train_samples: - validation_report["issues"].append("No training samples found") - validation_report["data_available"] = False - - except Exception as e: - validation_report["issues"].append(f"Data loading error: {e}") - validation_report["data_available"] = False - - # Test model configurations - only test models with available API keys - import os - test_configs = [{"name": "mock", "model_name": "mock"}] - - # Only test models if their API keys are available - if os.getenv("OPENAI_API_KEY"): - test_configs.append({"name": "gpt-4", "model_name": "gpt-4", "api_key": os.getenv("OPENAI_API_KEY")}) - - if os.getenv("ANTHROPIC_API_KEY"): - test_configs.append({"name": "claude-3-sonnet", "model_name": "claude-3-sonnet-20240229", "api_key": os.getenv("ANTHROPIC_API_KEY")}) - - for config in test_configs: - try: - model = create_model(**config) - is_available = model.is_available() - validation_report["models_accessible"][config["name"]] = is_available - - if not is_available and config["name"] != "mock": - validation_report["issues"].append(f"Model {config['name']} not accessible") - - except Exception as e: - validation_report["models_accessible"][config["name"]] = False - validation_report["issues"].append(f"Error testing {config['name']}: {e}") - - # Overall validation - if validation_report["issues"]: - validation_report["config_valid"] = False - - return validation_report - - def _generate_model_responses( - self, - model: LanguageModelInterface, - samples: List[BenchmarkSample], - split: str - ) -> Path: - """Generate responses for all samples and save to JSONL file.""" - from .evaluator import BenchmarkEvaluator - - # Create evaluator for response generation - evaluator = BenchmarkEvaluator( - model=model, - metrics=self.metrics, - schema=self.schema, - categorical_fields=self.categorical_fields - ) - - # Create output file - timestamp = int(time.time()) - filename = f"{model.model_name}_{split}_{timestamp}.jsonl" - filepath = self.config.output_dir / "responses" / filename - filepath.parent.mkdir(parents=True, exist_ok=True) - - logger.info(f"Generating responses to {filepath}") - - with open(filepath, 'w') as f: - for i, sample in enumerate(samples): - logger.info(f"Processing sample {i+1}/{len(samples)}: {sample.pmcid}") - - try: - # Generate prediction - prediction = evaluator.get_prediction_only(sample) - - if prediction is None: - logger.warning(f"Failed to generate prediction for {sample.pmcid}") - continue - - # Add metadata - response_data = { - "pmcid": sample.pmcid, - "model_name": model.model_name, - "timestamp": time.time(), - **prediction - } - - # Write to JSONL file - f.write(json.dumps(response_data) + '\n') - - except Exception as e: - logger.error(f"Error processing sample {sample.pmcid}: {e}") - continue - - logger.info(f"Response generation completed: {filepath}") - return filepath - - def _load_responses_from_file(self, response_file: Path) -> List[Dict[str, Any]]: - """Load responses from a JSONL file.""" - responses = [] - - with open(response_file, 'r') as f: - for line_num, line in enumerate(f, 1): - line = line.strip() - if not line: - continue - - try: - response = json.loads(line) - responses.append(response) - except json.JSONDecodeError as e: - logger.warning(f"Invalid JSON on line {line_num} in {response_file}: {e}") - continue - - logger.info(f"Loaded {len(responses)} responses from {response_file}") - return responses - - def _extract_split_from_filename(self, filepath: Path) -> str: - """Extract split name from response filename.""" - filename = filepath.stem - parts = filename.split('_') - - # Filename format: {model_name}_{split}_{timestamp} - if len(parts) >= 2: - return parts[-2] # Second to last part should be split - - return "test" # Default fallback - - def _save_intermediate_result(self, result: EvaluationResult, split: str) -> None: - """Save intermediate result to file.""" - timestamp = int(time.time()) - filename = f"{result.model_name}_{split}_{timestamp}.json" - filepath = self.config.output_dir / "intermediate" / filename - - filepath.parent.mkdir(parents=True, exist_ok=True) - - # Convert result to serializable format - result_dict = { - "model_name": result.model_name, - "total_samples": result.total_samples, - "successful_predictions": result.successful_predictions, - "failed_predictions": result.failed_predictions, - "aggregate_metrics": result.aggregate_metrics, - "error_analysis": result.error_analysis, - "timestamp": timestamp - } - - with open(filepath, 'w') as f: - json.dump(result_dict, f, indent=2) - - logger.info(f"Intermediate result saved to {filepath}") - - def get_data_statistics(self, split: str = "train") -> Dict[str, Any]: - """Get comprehensive statistics about the data.""" - samples = self.data_loader.load_split(split) - return self.data_loader.get_statistics(samples) - - def export_predictions( - self, - result: EvaluationResult, - split: str, - format: str = "jsonl" - ) -> Path: - """Export model predictions for analysis.""" - return self.reporter.export_predictions(result, split, format) \ No newline at end of file diff --git a/src/benchmark/reporting.py b/src/benchmark/reporting.py deleted file mode 100644 index ca1145c..0000000 --- a/src/benchmark/reporting.py +++ /dev/null @@ -1,393 +0,0 @@ -""" -Reporting and visualization utilities for benchmark results. -""" - -import json -import csv -from pathlib import Path -from typing import Dict, List, Any, Optional -from datetime import datetime -from loguru import logger - -from .evaluator import EvaluationResult - - -class BenchmarkReporter: - """Generate reports and visualizations for benchmark results.""" - - def __init__(self, output_dir: Path): - self.output_dir = output_dir - self.output_dir.mkdir(parents=True, exist_ok=True) - - def generate_comparative_report( - self, - results: Dict[str, EvaluationResult], - split: str - ) -> Path: - """Generate a comparative report across multiple models.""" - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - report_dir = self.output_dir / f"comparative_report_{split}_{timestamp}" - report_dir.mkdir(parents=True, exist_ok=True) - - # Generate summary report - summary_path = self._generate_summary_report(results, report_dir, split) - - # Generate detailed reports for each model - for model_name, result in results.items(): - self._generate_model_report(result, report_dir / f"{model_name}_detailed.html") - - # Generate performance comparison - self._generate_performance_comparison(results, report_dir / "performance_comparison.html") - - # Export raw data - self._export_raw_results(results, report_dir) - - logger.info(f"Comparative report generated at {report_dir}") - return report_dir - - def _generate_summary_report( - self, - results: Dict[str, EvaluationResult], - report_dir: Path, - split: str - ) -> Path: - """Generate HTML summary report.""" - html_content = f""" - - - - Benchmark Summary Report - {split.upper()} - - - -
-

Pharmacogenomic Benchmark Results

-

Split: {split.upper()}

-

Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}

-

Models Evaluated: {len(results)}

-
- -

Overall Performance Comparison

- - - - - - - - - - - - -""" - - # Sort models by weighted score - sorted_results = sorted( - results.items(), - key=lambda x: x[1].aggregate_metrics.get("mean_weighted_score", 0), - reverse=True - ) - - for model_name, result in sorted_results: - metrics = result.aggregate_metrics - overall_score = metrics.get("mean_overall_score", 0) - weighted_score = metrics.get("mean_weighted_score", 0) - success_rate = result.successful_predictions / result.total_samples * 100 - - score_class = self._get_score_class(weighted_score) - - html_content += f""" - - - - - - - - -""" - - html_content += """ - -
ModelOverall ScoreWeighted ScoreSuccessful PredictionsFailed PredictionsSuccess Rate
{model_name}{overall_score:.3f}{weighted_score:.3f}{result.successful_predictions}{len(result.failed_predictions)}{success_rate:.1f}%
- -

Field-Level Performance

-""" - - # Field-level comparison - if results: - # Get all fields from first result - first_result = next(iter(results.values())) - field_stats = first_result.aggregate_metrics.get("field_statistics", {}) - - html_content += """ - - - - -""" - for model_name in results.keys(): - html_content += f"" - html_content += f"" - - html_content += """ - - - -""" - - for field_name in sorted(field_stats.keys()): - html_content += f"" - - for model_name, result in results.items(): - field_data = result.aggregate_metrics.get("field_statistics", {}).get(field_name, {}) - mean_score = field_data.get("mean_score", 0) - exact_match_rate = field_data.get("exact_match_rate", 0) * 100 - - score_class = self._get_score_class(mean_score) - html_content += f'' - html_content += f'' - - html_content += "" - - html_content += """ - -
Field{model_name} (Score){model_name} (Exact Match %)
{field_name}{mean_score:.3f}{exact_match_rate:.1f}%
-""" - - # Model-specific sections - for model_name, result in sorted_results: - html_content += self._generate_model_section_html(model_name, result) - - html_content += """ - - -""" - - summary_path = report_dir / "summary.html" - with open(summary_path, 'w') as f: - f.write(html_content) - - return summary_path - - def _generate_model_section_html(self, model_name: str, result: EvaluationResult) -> str: - """Generate HTML section for a single model.""" - metrics = result.aggregate_metrics - - html = f""" -
-

{model_name}

-

Total Samples: {result.total_samples}

-

Successful Predictions: {result.successful_predictions}

-

Mean Overall Score: {metrics.get('mean_overall_score', 0):.3f}

-

Mean Weighted Score: {metrics.get('mean_weighted_score', 0):.3f}

- -

Score Distribution

-
    -""" - - score_dist = metrics.get("score_distribution", {}) - for category, count in score_dist.items(): - percentage = count / result.successful_predictions * 100 if result.successful_predictions > 0 else 0 - html += f"
  • {category.title()}: {count} ({percentage:.1f}%)
    • " - - html += """ -
- -

Top Field Issues

-
    -""" - - # Show fields with lowest scores - field_stats = metrics.get("field_statistics", {}) - sorted_fields = sorted(field_stats.items(), key=lambda x: x[1].get("mean_score", 0)) - - for field_name, stats in sorted_fields[:5]: # Top 5 problematic fields - mean_score = stats.get("mean_score", 0) - exact_match = stats.get("exact_match_rate", 0) * 100 - html += f"
  • {field_name}: Score {mean_score:.3f}, Exact Match {exact_match:.1f}%
    • " - - html += """ -
-
-""" - - return html - - def _get_score_class(self, score: float) -> str: - """Get CSS class for score styling.""" - if score >= 0.8: - return "high-score" - elif score >= 0.5: - return "medium-score" - else: - return "low-score" - - def _generate_model_report(self, result: EvaluationResult, output_path: Path) -> None: - """Generate detailed report for a single model.""" - # For now, create a simple JSON report - # In a full implementation, this would be a rich HTML report - - report_data = { - "model_name": result.model_name, - "summary": { - "total_samples": result.total_samples, - "successful_predictions": result.successful_predictions, - "success_rate": result.successful_predictions / result.total_samples, - "aggregate_metrics": result.aggregate_metrics - }, - "error_analysis": result.error_analysis, - "failed_predictions": result.failed_predictions - } - - with open(output_path.with_suffix('.json'), 'w') as f: - json.dump(report_data, f, indent=2) - - def _generate_performance_comparison( - self, - results: Dict[str, EvaluationResult], - output_path: Path - ) -> None: - """Generate performance comparison visualization.""" - # Create a simple comparison table - # In a full implementation, this would include charts and graphs - - comparison_data = {} - for model_name, result in results.items(): - comparison_data[model_name] = { - "overall_score": result.aggregate_metrics.get("mean_overall_score", 0), - "weighted_score": result.aggregate_metrics.get("mean_weighted_score", 0), - "success_rate": result.successful_predictions / result.total_samples, - "field_performance": result.aggregate_metrics.get("field_statistics", {}) - } - - with open(output_path.with_suffix('.json'), 'w') as f: - json.dump(comparison_data, f, indent=2) - - def _export_raw_results(self, results: Dict[str, EvaluationResult], output_dir: Path) -> None: - """Export raw results in various formats.""" - raw_dir = output_dir / "raw_data" - raw_dir.mkdir(exist_ok=True) - - # Export as JSON - for model_name, result in results.items(): - # Sample-level scores - sample_scores_data = [] - for sample_score in result.sample_scores: - sample_data = { - "pmcid": sample_score.pmcid, - "overall_score": sample_score.overall_score, - "weighted_score": sample_score.weighted_score, - "field_scores": [ - { - "field": fs.field_name, - "score": fs.score, - "exact_match": fs.exact_match, - "predicted": fs.predicted, - "expected": fs.expected, - "error_type": fs.error_type - } - for fs in sample_score.field_scores - ] - } - sample_scores_data.append(sample_data) - - with open(raw_dir / f"{model_name}_sample_scores.json", 'w') as f: - json.dump(sample_scores_data, f, indent=2) - - def export_predictions( - self, - result: EvaluationResult, - split: str, - format: str = "jsonl" - ) -> Path: - """Export model predictions for external analysis.""" - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - filename = f"{result.model_name}_{split}_predictions_{timestamp}" - - if format == "jsonl": - output_path = self.output_dir / f"{filename}.jsonl" - with open(output_path, 'w') as f: - for sample_score in result.sample_scores: - prediction_data = { - "pmcid": sample_score.pmcid, - "overall_score": sample_score.overall_score, - "predictions": { - fs.field_name: fs.predicted for fs in sample_score.field_scores - }, - "ground_truth": { - fs.field_name: fs.expected for fs in sample_score.field_scores - } - } - f.write(json.dumps(prediction_data) + '\n') - - elif format == "csv": - output_path = self.output_dir / f"{filename}.csv" - - if result.sample_scores: - # Get all field names - field_names = list(set( - fs.field_name for sample_score in result.sample_scores - for fs in sample_score.field_scores - )) - field_names.sort() - - with open(output_path, 'w', newline='') as f: - writer = csv.writer(f) - - # Header - header = ['pmcid', 'overall_score', 'weighted_score'] - for field in field_names: - header.extend([f"{field}_predicted", f"{field}_expected", f"{field}_score"]) - writer.writerow(header) - - # Data rows - for sample_score in result.sample_scores: - row = [sample_score.pmcid, sample_score.overall_score, sample_score.weighted_score] - - # Create field lookup - field_lookup = {fs.field_name: fs for fs in sample_score.field_scores} - - for field in field_names: - if field in field_lookup: - fs = field_lookup[field] - row.extend([fs.predicted, fs.expected, fs.score]) - else: - row.extend([None, None, 0.0]) - - writer.writerow(row) - - else: - raise ValueError(f"Unsupported export format: {format}") - - logger.info(f"Predictions exported to {output_path}") - return output_path - - def save_sample_analysis(self, analysis: Dict[str, Any]) -> Path: - """Save detailed analysis of a single sample.""" - pmcid = analysis["pmcid"] - model = analysis["model"] - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - - filename = f"sample_analysis_{pmcid}_{model}_{timestamp}.json" - output_path = self.output_dir / "sample_analyses" / filename - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(analysis, f, indent=2) - - logger.info(f"Sample analysis saved to {output_path}") - return output_path \ No newline at end of file From debc3d16236fa9e5a2fbe1c73789121453c7b59a Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Sat, 14 Jun 2025 15:31:49 -0700 Subject: [PATCH 10/28] feat: working variant list extraction test --- notebooks/test.ipynb | 141 +++--------------- src/annotation_extraction/README.md | 11 ++ src/annotation_extraction/components.py | 5 +- src/annotation_extraction/inference.py | 10 +- src/annotation_extraction/utils.py | 62 ++------ .../data}/true_variant_list.json | 0 tests/utils.py | 52 +++++++ tests/variant_list_tests.py | 69 +++++++++ 8 files changed, 177 insertions(+), 173 deletions(-) create mode 100644 src/annotation_extraction/README.md rename {data/benchmark => tests/data}/true_variant_list.json (100%) create mode 100644 tests/utils.py create mode 100644 tests/variant_list_tests.py diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb index f067187..4c08887 100644 --- a/notebooks/test.ipynb +++ b/notebooks/test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -48,42 +48,28 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'ArticleInput' from 'src.annotation_extraction.models' (/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/models.py)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msimple_inference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SimpleLLM, PromptGenerator\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mannotation_extraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01marticle_parser\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ArticleParser\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m List\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/__init__.py:8\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[33;03mAnnotation extraction module for pharmacogenomic variant annotations.\u001b[39;00m\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m \u001b[33;03mThis module implements a multi-stage LLM pipeline for extracting structured\u001b[39;00m\n\u001b[32m 5\u001b[39m \u001b[33;03mpharmacogenomic variant annotations from biomedical articles.\u001b[39;00m\n\u001b[32m 6\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m AnnotationPipeline\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01minference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 10\u001b[39m RelevanceScreener,\n\u001b[32m 11\u001b[39m EntityExtractor,\n\u001b[32m (...)\u001b[39m\u001b[32m 14\u001b[39m QualityValidator\n\u001b[32m 15\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 17\u001b[39m ArticleInput,\n\u001b[32m 18\u001b[39m RelevanceResult,\n\u001b[32m (...)\u001b[39m\u001b[32m 22\u001b[39m ValidationResult\n\u001b[32m 23\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/pipeline.py:10\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mconcurrent\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfutures\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ThreadPoolExecutor, as_completed\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdataclasses\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m asdict\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 11\u001b[39m ArticleInput, RelevanceResult, ExtractedEntities, ClassificationResult,\n\u001b[32m 12\u001b[39m AnnotationRow, ValidationResult, AnnotationType\n\u001b[32m 13\u001b[39m )\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01minference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 15\u001b[39m LLMInterface, RelevanceScreener, EntityExtractor, AnnotationClassifier,\n\u001b[32m 16\u001b[39m RowGenerator, QualityValidator\n\u001b[32m 17\u001b[39m )\n\u001b[32m 18\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mprompts\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m PromptTemplates\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'ArticleInput' from 'src.annotation_extraction.models' (/Users/shloknatarajan/stanford/research/daneshjou/AutoGKB/src/annotation_extraction/models.py)" - ] - } - ], + "outputs": [], "source": [ - "from src.annotation_extraction.simple_inference import SimpleLLM, PromptGenerator\n", + "from src.annotation_extraction.inference import SimpleLLM, PromptGenerator\n", "from src.annotation_extraction.article_parser import ArticleParser\n", "from typing import List" ] }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-13 13:40:33.257\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m13\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-13 13:40:33.261\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m121\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-13 13:40:33.262\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m13\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-13 13:40:33.264\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m121\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" + "\u001b[32m2025-06-14 14:27:20.483\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-14 14:27:20.492\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-14 14:27:20.493\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-14 14:27:20.494\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" ] } ], @@ -94,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -133,8 +119,7 @@ "\n", "{article_text}\n", "\n", - "From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.) that are part of the article's\n", - "study/analysis. Include information on the gene group and allele (if present). Your output format should be a list of the variants with the following attributes:\n", + "From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.). Include information on the gene group and allele (if present). Your output format should be a list of the variants with the following attributes:\n", "Variant: The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18, etc.)\n", "Gene: The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11, etc.)\n", "Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.)\n", @@ -143,84 +128,30 @@ }, { "cell_type": "code", - "execution_count": 286, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from dotenv import load_dotenv\n", "load_dotenv()\n", - "from src.annotation_extraction.simple_inference import VariantList, Variant\n", + "from src.annotation_extraction.inference import VariantList, Variant\n", "import json\n", - "from loguru import logger" + "from loguru import logger\n", + "from src.annotation_extraction.utils import save_output\n", + "from src.annotation_extraction.components import extract_variants_list\n" ] }, { "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [], - "source": [ - "def save_output(prompt, output, filename):\n", - " # save prompt and output to file\n", - " with open(f\"test_outputs/{filename}.txt\", \"w\") as f:\n", - " f.write(\"Prompt:\\n\")\n", - " f.write(prompt)\n", - " f.write(\"\\nOutput:\\n\")\n", - " f.write(output)\n", - " logger.info(f\"Saved output to {filename}.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [], - "source": [ - "from src.annotation_extraction.simple_inference import Variant, VariantList" - ] - }, - { - "cell_type": "code", - "execution_count": 169, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-13 14:04:22.642\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m13\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-13 14:04:22.645\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.models\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m121\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" - ] - } - ], - "source": [ - "model = SimpleLLM()\n", - "article_text = ArticleParser(pmcid=\"PMC11730665\").get_article_text()\n", - "prompt_generator = PromptGenerator(VARIANT_LIST_PROMPT, {\"article_text\": article_text})\n", - "prompt = prompt_generator.get_prompt()\n", - "output = model.generate(prompt, response_format=VariantList)" - ] - }, - { - "cell_type": "code", - "execution_count": 289, - "metadata": {}, - "outputs": [], - "source": [ - "from src.annotation_extraction.components import extract_variants_list" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-13 14:11:26.359\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-13 14:11:26.361\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" + "\u001b[32m2025-06-14 14:32:19.229\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-14 14:32:19.232\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.annotation_extraction.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" ] } ], @@ -258,37 +189,11 @@ }, { "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-12 14:57:23.200\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36msave_output\u001b[0m:\u001b[36m8\u001b[0m - \u001b[1mSaved output to variant_list_2.txt\u001b[0m\n" - ] - } - ], - "source": [ - "# save summary to file\n", - "save_output(prompt_generator.get_prompt_template(), output, \"variant_list_2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [], - "source": [ - "pmcid_grouped = json.load(open(\"data/variantAnnotations/annotations_by_pmcid.json\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 197, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ + "pmcid_grouped = json.load(open(\"data/variantAnnotations/annotations_by_pmcid.json\"))\n", "true_variant_list = {}\n", "for paper in pmcid_grouped:\n", " variants = []\n", diff --git a/src/annotation_extraction/README.md b/src/annotation_extraction/README.md new file mode 100644 index 0000000..1b3f493 --- /dev/null +++ b/src/annotation_extraction/README.md @@ -0,0 +1,11 @@ +# Annotation Extraction + + +## Examples +``` +model = SimpleLLM() +article_text = ArticleParser(pmcid="PMC11730665").get_article_text() +prompt_generator = PromptGenerator(, {"article_text": article_text}) +prompt = prompt_generator.get_prompt() +output = model.generate(prompt, response_format=) +``` \ No newline at end of file diff --git a/src/annotation_extraction/components.py b/src/annotation_extraction/components.py index 248c561..0cf3813 100644 --- a/src/annotation_extraction/components.py +++ b/src/annotation_extraction/components.py @@ -1,4 +1,4 @@ -from src.annotation_extraction.simple_inference import Variant, VariantList, SimpleLLM, PromptGenerator +from src.annotation_extraction.inference import Variant, VariantList, SimpleLLM, PromptGenerator from src.annotation_extraction.article_parser import ArticleParser from loguru import logger import json @@ -8,8 +8,7 @@ {article_text} -From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.) that are part of the article's -study/analysis. Include information on the gene group and allele (if present). Your output format should be a list of the variants with the following attributes: +From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.). Include information on the gene group and allele (if present). Your output format should be a list of the variants with the following attributes: Variant: The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18, etc.) Gene: The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11, etc.) Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.) diff --git a/src/annotation_extraction/inference.py b/src/annotation_extraction/inference.py index b7ff70f..eaff4c7 100644 --- a/src/annotation_extraction/inference.py +++ b/src/annotation_extraction/inference.py @@ -1,7 +1,6 @@ from typing import Dict from loguru import logger import litellm -from .inference import LLMInterface import re import os from dotenv import load_dotenv @@ -34,6 +33,15 @@ def get_prompt(self) -> str: def get_prompt_template(self) -> str: return self.prompt_template +class LLMInterface: + """LLM interface.""" + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): + self.model = model + self.temperature = temperature + + def generate(self, prompt: str, temperature: float = None) -> str: + raise NotImplementedError("Subclasses must implement this method.") + class SimpleLLM(LLMInterface): """Simple LLM interface that just returns as response to the prompt.""" debug_mode = False diff --git a/src/annotation_extraction/utils.py b/src/annotation_extraction/utils.py index 8948ddb..19056ab 100644 --- a/src/annotation_extraction/utils.py +++ b/src/annotation_extraction/utils.py @@ -1,58 +1,18 @@ import re from termcolor import colored -# Example usage +from loguru import logger + def extractVariants(text): # Note, seems to extract a ton of variants, not just the ones that are being studied + # Think it might only be applicable to rsIDs variantRegex = r'\b([A-Z]+\d+[A-Z]*\*\d+|\brs\d+)\b' return re.findall(variantRegex, text) or [] - -def compare_lists(experimental_list, ground_truth_list, pmcid): - """ - Compare experimental list with ground truth list and calculate performance metrics. - - Args: - experimental_list (list): List of predicted/experimental values - ground_truth_list (list): List of actual/ground truth values - pmcid (str): PMCID of the article - - Returns: - tuple: (true_positives, true_negatives, false_positives, false_negatives) - """ - # Convert lists to sets for efficient comparison - experimental_set = set(experimental_list) - ground_truth_set = set(ground_truth_list) - - # Calculate performance metrics - true_positives = len(experimental_set.intersection(ground_truth_set)) - false_positives = len(experimental_set - ground_truth_set) - false_negatives = len(ground_truth_set - experimental_set) - true_negatives = 0 # Not applicable in this context - - # Color-code the lists - colored_experimental = [] - colored_ground_truth = [] - - # Color experimental list - for item in experimental_list: - if item in ground_truth_set: - colored_experimental.append(colored(item, 'green')) - else: - colored_experimental.append(colored(item, 'red')) - - # Color ground truth list - for item in ground_truth_list: - if item in experimental_set: - colored_ground_truth.append(colored(item, 'green')) - else: - colored_ground_truth.append(colored(item, 'red')) - - # Print colored lists - print(f"================= {pmcid} =================") - print("Experimental List:") - print(' '.join(map(str, colored_experimental))) - print("\nGround Truth List:") - print(' '.join(map(str, colored_ground_truth))) - - # Return performance metrics - return true_positives, true_negatives, false_positives, false_negatives \ No newline at end of file +def save_output(prompt, output, filename): + # save prompt and output to file + with open(f"test_outputs/{filename}.txt", "w") as f: + f.write("Prompt:\n") + f.write(prompt) + f.write("\nOutput:\n") + f.write(output) + logger.info(f"Saved output to {filename}.txt") \ No newline at end of file diff --git a/data/benchmark/true_variant_list.json b/tests/data/true_variant_list.json similarity index 100% rename from data/benchmark/true_variant_list.json rename to tests/data/true_variant_list.json diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 0000000..f8cd412 --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,52 @@ +from termcolor import colored +from typing import List + +def compare_lists(experimental_list: List[str], ground_truth_list: List[str], pmcid: str): + """ + Compare experimental list with ground truth list and calculate performance metrics. + + Args: + experimental_list (list): List of predicted/experimental values + ground_truth_list (list): List of actual/ground truth values + pmcid (str): PMCID of the article + + Returns: + tuple: (true_positives, true_negatives, false_positives, false_negatives) + """ + # Convert lists to sets for efficient comparison + experimental_set = set(experimental_list) + ground_truth_set = set(ground_truth_list) + + # Calculate performance metrics + true_positives = len(experimental_set.intersection(ground_truth_set)) + false_positives = len(experimental_set - ground_truth_set) + false_negatives = len(ground_truth_set - experimental_set) + true_negatives = 0 # Not applicable in this context + + # Color-code the lists + colored_experimental = [] + colored_ground_truth = [] + + # Color experimental list + for item in experimental_list: + if item in ground_truth_set: + colored_experimental.append(colored(item, 'green')) + else: + colored_experimental.append(colored(item, 'red')) + + # Color ground truth list + for item in ground_truth_list: + if item in experimental_set: + colored_ground_truth.append(colored(item, 'green')) + else: + colored_ground_truth.append(colored(item, 'red')) + + # Print colored lists + print(f"================= {pmcid} =================") + print("Experimental List:") + print(' '.join(map(str, colored_experimental))) + print("\nGround Truth List:") + print(' '.join(map(str, colored_ground_truth))) + + # Return performance metrics + return true_positives, true_negatives, false_positives, false_negatives \ No newline at end of file diff --git a/tests/variant_list_tests.py b/tests/variant_list_tests.py new file mode 100644 index 0000000..5432626 --- /dev/null +++ b/tests/variant_list_tests.py @@ -0,0 +1,69 @@ +from loguru import logger +from src.annotation_extraction.components import extract_variants_list +import json +from typing import List +from tests.utils import compare_lists + +def load_ground_truth(pmcid: str): + try: + with open(f"tests/data/true_variant_list.json", "r") as f: + try: + return json.load(f)[pmcid] + except KeyError: + logger.error(f"PMCID {pmcid} not found in ground truth file (tests/data/true_variant_list.json)") + return [] + except FileNotFoundError: + logger.error(f"Ground truth file for PMCID {pmcid} not found (tests/data/true_variant_list.json)") + return [] + +def parse_variant_list(variant_list): + return [i['variant_id'] for i in variant_list] + +def calc_contingencies(ground_truth: List[str], extracted: List[str]): + true_positives = len(set(ground_truth) & set(extracted)) + true_negatives = len(set(extracted) - set(ground_truth)) + false_positives = len(set(extracted) - set(ground_truth)) + false_negatives = len(set(ground_truth) - set(extracted)) + return { + "true_positives": true_positives, + "true_negatives": true_negatives, + "false_positives": false_positives, + "false_negatives": false_negatives, + } + +def calc_metrics(contingencies): + precision = contingencies["true_positives"] / (contingencies["true_positives"] + contingencies["false_positives"]) + recall = contingencies["true_positives"] / (contingencies["true_positives"] + contingencies["false_negatives"]) + f1_score = 2 * (precision * recall) / (precision + recall) + return precision, recall, f1_score + +def test_extract_function(pmcids: List[str] | str, verbose: bool = False): + running_contingencies = { + "true_positives": 0, + "true_negatives": 0, + "false_positives": 0, + "false_negatives": 0, + } + # Test the extract function + if isinstance(pmcids, str): + pmcids = [pmcids] + + for pmcid in pmcids: + logger.info(f"Testing PMCID: {pmcid}") + ground_truth = parse_variant_list(load_ground_truth(pmcid)) + extracted = parse_variant_list(extract_variants_list(pmcid)) + contingencies = calc_contingencies(ground_truth, extracted) + # update running contingencies + running_contingencies["true_positives"] += contingencies["true_positives"] + running_contingencies["true_negatives"] += contingencies["true_negatives"] + running_contingencies["false_positives"] += contingencies["false_positives"] + running_contingencies["false_negatives"] += contingencies["false_negatives"] + if verbose: + compare_lists(extracted, ground_truth, pmcid) + + # calculate final metrics + precision, recall, f1_score = calc_metrics(running_contingencies) + logger.info(f"Final Metrics: Precision: {precision}, Recall: {recall}, F1 Score: {f1_score}") + +if __name__ == "__main__": + test_extract_function("PMC11730665", verbose=True) \ No newline at end of file From 595f087c9a5e0364b7d162a284ffad65cf73a573 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Sat, 14 Jun 2025 17:25:38 -0700 Subject: [PATCH 11/28] docs: readme updates --- tests/README.md | 37 +++++++++++++++++++++++++++++++++++++ tests/variant_list_tests.py | 2 +- 2 files changed, 38 insertions(+), 1 deletion(-) create mode 100644 tests/README.md diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 0000000..b2b0bda --- /dev/null +++ b/tests/README.md @@ -0,0 +1,37 @@ +# Tests + +This directory contains test files for the AutoGKB project. + +## Files + +- `variant_list_tests.py` - Tests for variant list extraction functionality +- `utils.py` - Utility functions for test comparisons +- `data/true_variant_list.json` - Ground truth data for variant list testing + +## Variant List Tests + +The `variant_list_tests.py` module tests the variant extraction component: + +- `load_ground_truth(pmcid)` - Loads ground truth variant data for a given PMCID +- `parse_variant_list(variant_list)` - Extracts variant IDs from variant list data (variant_list is a list of class Variant) +- `calc_contingencies(ground_truth, extracted)` - Calculates TP, TN, FP, FN metrics comparing two lists of strings +- `calc_metrics(contingencies)` - Calculates precision, recall, and F1 score from {"true_positives": int, "true_negatives: int, ...} +- `test_extract_function(pmcids, verbose)` - Main test function that evaluates extraction performance + +## Test Utilities + +The `utils.py` module provides: + +- `compare_lists(experimental_list, ground_truth_list, pmcid)` - Visually compares experimental vs ground truth lists with color coding (green for matches, red for mismatches) + +## Running Tests + +```python +from tests.variant_list_tests import test_extract_function + +# Test single PMCID +test_extract_function("PMC11730665", verbose=True) + +# Test multiple PMCIDs +test_extract_function(["PMC11730665", "PMC5712579"]) +``` \ No newline at end of file diff --git a/tests/variant_list_tests.py b/tests/variant_list_tests.py index 5432626..8995afe 100644 --- a/tests/variant_list_tests.py +++ b/tests/variant_list_tests.py @@ -63,7 +63,7 @@ def test_extract_function(pmcids: List[str] | str, verbose: bool = False): # calculate final metrics precision, recall, f1_score = calc_metrics(running_contingencies) - logger.info(f"Final Metrics: Precision: {precision}, Recall: {recall}, F1 Score: {f1_score}") + print(f"Final Metrics: Precision: {precision}, Recall: {recall}, F1 Score: {f1_score}") if __name__ == "__main__": test_extract_function("PMC11730665", verbose=True) \ No newline at end of file From 764c84809fab782707ce52c0309d20475081b64a Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 12:15:28 -0700 Subject: [PATCH 12/28] chore: removed unused files --- src/annotation_extraction/example_usage.py | 274 -------------- src/annotation_extraction/pipeline.py | 421 --------------------- 2 files changed, 695 deletions(-) delete mode 100644 src/annotation_extraction/example_usage.py delete mode 100644 src/annotation_extraction/pipeline.py diff --git a/src/annotation_extraction/example_usage.py b/src/annotation_extraction/example_usage.py deleted file mode 100644 index 88732ba..0000000 --- a/src/annotation_extraction/example_usage.py +++ /dev/null @@ -1,274 +0,0 @@ -""" -Example usage of the annotation extraction pipeline. - -This script demonstrates how to use the annotation extraction system -to process biomedical articles and generate pharmacogenomic annotations. -""" - -import os -import json -from loguru import logger -from typing import List - -from .models import ArticleInput, ArticleParser -from .pipeline import AnnotationPipeline -from .stages import LLMInterface -from dotenv import load_dotenv - -load_dotenv() - - -class LiteLLMInterface(LLMInterface): - """ - LiteLLM interface implementation. - - Uses LiteLLM to support multiple LLM providers with a unified interface. - """ - - def __init__(self, model: str = "gpt-3.5-turbo", **kwargs): - """ - Initialize LiteLLM interface. - - Args: - model: Model name (e.g., "gpt-4", "claude-3-sonnet-20240229", "ollama/llama2") - **kwargs: Additional parameters for the model - """ - try: - import litellm - self.litellm = litellm - except ImportError: - raise ImportError("litellm is required. Install with: pip install litellm") - - self.model = model - self.kwargs = kwargs - - # Set API keys from environment variables - if model.startswith("gpt"): - api_key = os.getenv("OPENAI_API_KEY") - if api_key: - os.environ["OPENAI_API_KEY"] = api_key - elif model.startswith("claude"): - api_key = os.getenv("ANTHROPIC_API_KEY") - if api_key: - os.environ["ANTHROPIC_API_KEY"] = api_key - elif model.startswith("azure"): - api_key = os.getenv("AZURE_API_KEY") - if api_key: - os.environ["AZURE_API_KEY"] = api_key - - def generate(self, prompt: str, temperature: float = 0.1) -> str: - """Generate response from LLM using LiteLLM.""" - try: - response = self.litellm.completion( - model=self.model, - messages=[{"role": "user", "content": prompt}], - temperature=temperature, - **self.kwargs - ) - return response.choices[0].message.content - except Exception as e: - logger.error(f"LiteLLM generation error: {e}") - # Fallback to mock response for testing - return self._mock_response(prompt) - - def _mock_response(self, prompt: str) -> str: - """Mock responses for testing when LLM fails.""" - if "RELEVANT or NOT_RELEVANT" in prompt: - return "RELEVANT\nThis article discusses CYP2D6 variants and their effects on drug metabolism." - - elif "Extract:" in prompt and "GENETIC VARIANTS:" in prompt: - return """GENETIC VARIANTS: -- CYP2D6*4 -- rs1065852 -- CYP2C19*2/*3 - -DRUGS AND INTERVENTIONS: -- codeine -- tramadol -- omeprazole - -PHENOTYPES AND OUTCOMES: -- reduced metabolism -- poor metabolizer phenotype -- therapeutic failure - -POPULATION INFORMATION: -- European ancestry (n=150) -- Adults aged 18-65 - -ASSOCIATIONS AND RELATIONSHIPS: -- CYP2D6*4 associated with reduced codeine metabolism (p<0.001) -- Poor metabolizers show 50% reduced drug clearance""" - - elif "Classification Rules:" in prompt: - return """1 | functional, drug | high | In vitro metabolism study with clinical correlation -2 | phenotype | medium | Adverse event association in patient cohort""" - - elif "REQUIRED OUTPUT FORMAT - Tab-separated values" in prompt: - return """1451148445 CYP2D6*4 CYP2D6 codeine 12345678 Metabolism/PK yes In vitro metabolism study CYP2D6*4 is associated with reduced metabolism of codeine. *4 human liver microsomes poor metabolizer Is reduced metabolism of CYP2D6 when assayed with codeine hepatocytes *1/*1 extensive metabolizer""" - - elif "VALID or INVALID" in prompt: - return "VALID\nAll required fields are present and follow the schema." - - else: - return "Mock LLM response for: " + prompt[:100] + "..." - -def get_example_articles(num_articles: int = 2) -> List[ArticleInput]: - """Get example articles for testing.""" - # 1. Load n random markdown files from the data/articles directory - # 2. Parse the articles - # 3. Return the articles - articles = [] - for i in range(num_articles): - with open(f"data/articles/article_{i}.md", "r") as f: - articles.append(f.read()) - - return articles - - -def create_example_articles() -> List[ArticleInput]: - """Create example articles for testing.""" - - article1 = ArticleParser(pmcid="PMC16264").parse() - article2 = ArticleParser(pmcid="PMC11534822").parse() - return [article1, article2] - -def main(): - """Main example usage function.""" - - # Initialize LiteLLM interface - # Examples of different model configurations: - - # OpenAI GPT-4 - # llm = LiteLLMInterface(model="gpt-4") - - # Anthropic Claude - llm = LiteLLMInterface(model="claude-3-sonnet-20240229") - - # Local Ollama model - # llm = LiteLLMInterface(model="ollama/llama2") - - # Azure OpenAI - # llm = LiteLLMInterface(model="azure/gpt-4") - - # Default to GPT-3.5-turbo for this example - llm = LiteLLMInterface( - model="gpt-3.5-turbo", - max_tokens=4000 - ) - - # Create pipeline - pipeline = AnnotationPipeline( - llm=llm, - enable_validation=True, - parallel_processing=False # Set to True for parallel processing - ) - - # Create example articles - articles = create_example_articles() - - logger.info(f"Processing {len(articles)} example articles...") - - # Process articles - results = pipeline.process_articles(articles) - - # Display results - for result in results: - print(f"\n=== Article PMID: {result['pmid']} ===") - print(f"Success: {result['success']}") - - if result.get('relevance'): - print(f"Relevant: {result['relevance']['is_relevant']}") - if result['relevance']['is_relevant'] and result['relevance'].get('summary'): - print(f"Summary: {result['relevance']['summary']}") - - if result.get('annotations'): - print(f"Generated {len(result['annotations'])} annotations:") - for i, annotation in enumerate(result['annotations']): - print(f" {i+1}. Type: {annotation['annotation_type']}") - print(f" Fields: {len(annotation['row_data'])}") - # Show first few fields - if len(annotation['row_data']) >= 4: - print(f" Variant: {annotation['row_data'][1]}") - print(f" Gene: {annotation['row_data'][2]}") - print(f" Drug: {annotation['row_data'][3]}") - - if result.get('errors'): - print(f"Errors: {result['errors']}") - - # Get pipeline statistics - stats = pipeline.get_pipeline_stats(results) - print(f"\n=== Pipeline Statistics ===") - print(f"Total articles: {stats['total_articles']}") - print(f"Successful: {stats['successful_articles']}") - print(f"Relevant: {stats['relevant_articles']}") - print(f"Total annotations: {stats['total_annotations']}") - print(f"Annotations by type: {stats['annotations_by_type']}") - print(f"Validation pass rate: {stats['validation_pass_rate']:.2%}") - - # Example: Export to TSV files - output_dir = "example_output" - os.makedirs(output_dir, exist_ok=True) - - file_paths = pipeline.export_annotations_to_tsv(results, output_dir) - print(f"\n=== Exported Files ===") - for annotation_type, file_path in file_paths.items(): - print(f"{annotation_type}: {file_path}") - - # Example: Batch processing - print("\n=== Batch Processing Example ===") - if articles: - batch_result = pipeline.process_batch_article(articles[0]) - print(f"Batch processing success: {batch_result['success']}") - if batch_result.get('batch_annotations'): - print(f"Batch annotations: {len(batch_result['batch_annotations'])}") - - -def example_different_models(): - """ - Example showing different LLM model configurations with LiteLLM. - """ - - models_to_test = [ - { - "name": "GPT-3.5 Turbo", - "model": "gpt-3.5-turbo" - }, - { - "name": "GPT-4", - "model": "gpt-4" - }, - { - "name": "Claude 3 Sonnet", - "model": "claude-3-sonnet-20240229" - } - ] - - articles = create_example_articles()[:1] # Test with one article - - for model_config in models_to_test: - print(f"\n=== Testing {model_config['name']} ===") - - try: - llm = LiteLLMInterface( - model=model_config["model"], - max_tokens=2000 - ) - - pipeline = AnnotationPipeline(llm=llm, enable_validation=False) - results = pipeline.process_articles(articles) - - if results and results[0].get('success'): - print(f"✓ Success: {len(results[0].get('annotations', []))} annotations") - else: - print(f"✗ Failed: {results[0].get('errors', []) if results else 'No results'}") - - except Exception as e: - print(f"✗ Error: {e}") - - -if __name__ == "__main__": - main() - - # Uncomment to test different models - # example_different_models() \ No newline at end of file diff --git a/src/annotation_extraction/pipeline.py b/src/annotation_extraction/pipeline.py deleted file mode 100644 index ea02c1c..0000000 --- a/src/annotation_extraction/pipeline.py +++ /dev/null @@ -1,421 +0,0 @@ -""" -Main pipeline orchestrator for annotation extraction. -""" - -from typing import List, Dict, Optional, Tuple -from loguru import logger -from concurrent.futures import ThreadPoolExecutor, as_completed -from dataclasses import asdict - -from .models import ( - RelevanceResult, ExtractedEntities, ClassificationResult, - AnnotationRow, ValidationResult, AnnotationType -) -from .inference import ( - LLMInterface, RelevanceScreener, EntityExtractor, AnnotationClassifier, - RowGenerator, QualityValidator -) -from .prompts import PromptTemplates - - -class AnnotationPipeline: - """Main pipeline for extracting pharmacogenomic annotations from articles.""" - - def __init__(self, llm: LLMInterface, enable_validation: bool = True, - parallel_processing: bool = False, max_workers: int = 4): - """ - Initialize the annotation pipeline. - - Args: - llm: LLM interface for generating responses - enable_validation: Whether to enable quality validation - parallel_processing: Whether to process multiple articles in parallel - max_workers: Maximum number of worker threads for parallel processing - """ - self.llm = llm - self.enable_validation = enable_validation - self.parallel_processing = parallel_processing - self.max_workers = max_workers - - # Initialize pipeline stages - self.relevance_screener = RelevanceScreener(llm) - self.entity_extractor = EntityExtractor(llm) - self.annotation_classifier = AnnotationClassifier(llm) - self.row_generator = RowGenerator(llm) - self.quality_validator = QualityValidator(llm) if enable_validation else None - - def process_article(self, article: ArticleInput) -> Dict: - """ - Process a single article through the complete pipeline. - - Args: - article: Input article data - - Returns: - Dictionary containing processing results and generated annotations - """ - logger.info(f"Processing article PMID: {article.pmid}") - - results = { - 'pmid': article.pmid, - 'relevance': None, - 'entities': None, - 'classifications': [], - 'annotations': [], - 'validation_results': [], - 'errors': [], - 'success': False - } - - try: - # Stage 1: Relevance screening - logger.debug("Stage 1: Relevance screening") - relevance = self.relevance_screener.screen_article(article) - results['relevance'] = asdict(relevance) - - if not relevance.is_relevant: - logger.info(f"Article {article.pmid} not relevant for annotation") - results['success'] = True - return results - - # Stage 2: Entity extraction - logger.debug("Stage 2: Entity extraction") - entities = self.entity_extractor.extract_entities(article) - results['entities'] = asdict(entities) - - # Stage 3: Relationship classification - logger.debug("Stage 3: Relationship classification") - classifications = self.annotation_classifier.classify_relationships(entities, article) - results['classifications'] = [asdict(c) for c in classifications] - - if not classifications: - logger.warning(f"No classifiable relationships found in article {article.pmid}") - results['success'] = True - return results - - # Stage 4: Row generation - logger.debug("Stage 4: Row generation") - annotations = self.row_generator.generate_annotation_rows(classifications, article) - results['annotations'] = [asdict(a) for a in annotations] - - # Stage 5: Quality validation (optional) - if self.enable_validation and self.quality_validator: - logger.debug("Stage 5: Quality validation") - validation_results = [] - for annotation in annotations: - validation = self.quality_validator.validate_row(annotation, article) - validation_results.append(validation) - - results['validation_results'] = [asdict(v) for v in validation_results] - - # Filter out invalid annotations if validation is enabled - valid_annotations = [] - for annotation, validation in zip(annotations, validation_results): - if validation.is_valid: - valid_annotations.append(annotation) - else: - logger.warning(f"Invalid annotation for {article.pmid}: {validation.errors}") - - results['annotations'] = [asdict(a) for a in valid_annotations] - - results['success'] = True - logger.info(f"Successfully processed article {article.pmid}: {len(results['annotations'])} annotations") - - except Exception as e: - logger.error(f"Error processing article {article.pmid}: {str(e)}") - results['errors'].append(str(e)) - results['success'] = False - - return results - - def process_articles(self, articles: List[ArticleInput]) -> List[Dict]: - """ - Process multiple articles through the pipeline. - - Args: - articles: List of input articles - - Returns: - List of processing results for each article - """ - logger.info(f"Processing {len(articles)} articles") - - if self.parallel_processing and len(articles) > 1: - return self._process_articles_parallel(articles) - else: - return self._process_articles_sequential(articles) - - def _process_articles_sequential(self, articles: List[ArticleInput]) -> List[Dict]: - """Process articles sequentially.""" - results = [] - for article in articles: - result = self.process_article(article) - results.append(result) - return results - - def _process_articles_parallel(self, articles: List[ArticleInput]) -> List[Dict]: - """Process articles in parallel.""" - results = [] - - with ThreadPoolExecutor(max_workers=self.max_workers) as executor: - # Submit all articles for processing - future_to_article = { - executor.submit(self.process_article, article): article - for article in articles - } - - # Collect results as they complete - for future in as_completed(future_to_article): - article = future_to_article[future] - try: - result = future.result() - results.append(result) - except Exception as e: - logger.error(f"Error processing article {article.pmid}: {str(e)}") - results.append({ - 'pmid': article.pmid, - 'success': False, - 'errors': [str(e)] - }) - - # Sort results by PMID to maintain consistent ordering - results.sort(key=lambda x: x.get('pmid', '')) - - return results - - def process_batch_article(self, article: ArticleInput) -> Dict: - """ - Process an article using batch processing for multiple relationships. - - This method uses the batch processing prompt to handle articles - with many variant-outcome relationships more efficiently. - - Args: - article: Input article data - - Returns: - Dictionary containing processing results - """ - logger.info(f"Batch processing article PMID: {article.pmid}") - - results = { - 'pmid': article.pmid, - 'relevance': None, - 'batch_annotations': [], - 'validation_results': [], - 'errors': [], - 'success': False - } - - try: - # Stage 1: Relevance screening - relevance = self.relevance_screener.screen_article(article) - results['relevance'] = asdict(relevance) - - if not relevance.is_relevant: - logger.info(f"Article {article.pmid} not relevant for batch annotation") - results['success'] = True - return results - - # Batch processing prompt - prompt = PromptTemplates.format_prompt( - PromptTemplates.BATCH_PROCESSING, - pmid=article.pmid, - full_article=article.article_text - ) - - response = self.llm.generate(prompt, temperature=0.2) - - # Parse batch response - annotations = self._parse_batch_response(response) - results['batch_annotations'] = [asdict(a) for a in annotations] - - # Optional validation - if self.enable_validation and self.quality_validator: - validation_results = [] - for annotation in annotations: - validation = self.quality_validator.validate_row(annotation, article) - validation_results.append(validation) - - results['validation_results'] = [asdict(v) for v in validation_results] - - # Filter valid annotations - valid_annotations = [] - for annotation, validation in zip(annotations, validation_results): - if validation.is_valid: - valid_annotations.append(annotation) - - results['batch_annotations'] = [asdict(a) for a in valid_annotations] - - results['success'] = True - logger.info(f"Batch processed article {article.pmid}: {len(results['batch_annotations'])} annotations") - - except Exception as e: - logger.error(f"Error batch processing article {article.pmid}: {str(e)}") - results['errors'].append(str(e)) - results['success'] = False - - return results - - def _parse_batch_response(self, response: str) -> List[AnnotationRow]: - """Parse batch processing response into annotation rows.""" - annotations = [] - lines = response.strip().split('\n') - - current_type = None - - for line in lines: - line = line.strip() - - if line.startswith('ANNOTATION_TYPE:'): - type_text = line.split(':', 1)[1].strip().lower() - if 'functional' in type_text: - current_type = AnnotationType.FUNCTIONAL - elif 'drug' in type_text: - current_type = AnnotationType.DRUG - elif 'phenotype' in type_text: - current_type = AnnotationType.PHENOTYPE - - elif line.startswith('ROW:') and current_type: - row_data = line.split(':', 1)[1].strip() - if '\t' in row_data: - fields = self._get_field_names_for_type(current_type) - annotation = AnnotationRow( - annotation_type=current_type, - row_data=row_data.split('\t'), - fields=fields - ) - annotations.append(annotation) - - return annotations - - def _get_field_names_for_type(self, annotation_type: AnnotationType) -> List[str]: - """Get field names for annotation type.""" - if annotation_type == AnnotationType.FUNCTIONAL: - return [ - "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", - "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", - "Specialty Population", "Assay type", "Metabolizer types", "isPlural", - "Is/Is Not associated", "Direction of effect", "Functional terms", - "Gene/gene product", "When treated with/exposed to/when assayed with", - "Multiple drugs And/or", "Cell type", "Comparison Allele(s) or Genotype(s)", - "Comparison Metabolizer types" - ] - elif annotation_type == AnnotationType.DRUG: - return [ - "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", - "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", - "Specialty Population", "Metabolizer types", "isPlural", - "Is/Is Not associated", "Direction of effect", "PD/PK terms", - "Multiple drugs And/or", "Population types", - "Population Phenotypes or diseases", "Multiple phenotypes or diseases And/or", - "Comparison Allele(s) or Genotype(s)", "Comparison Metabolizer types" - ] - elif annotation_type == AnnotationType.PHENOTYPE: - return [ - "Variant Annotation ID", "Variant/Haplotypes", "Gene", "Drug(s)", "PMID", - "Phenotype Category", "Significance", "Notes", "Sentence", "Alleles", - "Specialty Population", "Metabolizer types", "isPlural", - "Is/Is Not associated", "Direction of effect", "Side effect/efficacy/other", - "Phenotype", "Multiple phenotypes And/or", - "When treated with/exposed to/when assayed with", "Multiple drugs And/or", - "Population types", "Population Phenotypes or diseases", - "Multiple phenotypes or diseases And/or", - "Comparison Allele(s) or Genotype(s)", "Comparison Metabolizer types" - ] - else: - return [] - - def export_annotations_to_tsv(self, results: List[Dict], output_dir: str) -> Dict[str, str]: - """ - Export annotations to TSV files by type. - - Args: - results: List of processing results - output_dir: Directory to save TSV files - - Returns: - Dictionary mapping annotation types to file paths - """ - import os - from collections import defaultdict - - # Group annotations by type - annotations_by_type = defaultdict(list) - - for result in results: - if result.get('success') and result.get('annotations'): - for annotation_dict in result['annotations']: - annotation_type = annotation_dict['annotation_type'] - annotations_by_type[annotation_type].append(annotation_dict) - - # Write TSV files - file_paths = {} - - for annotation_type, annotations in annotations_by_type.items(): - if not annotations: - continue - - filename = f"var_{annotation_type}_ann.tsv" - file_path = os.path.join(output_dir, filename) - - with open(file_path, 'w', encoding='utf-8') as f: - # Write header - if annotations: - header = '\t'.join(annotations[0]['fields']) - f.write(header + '\n') - - # Write data rows - for annotation in annotations: - row = '\t'.join(annotation['row_data']) - f.write(row + '\n') - - file_paths[annotation_type] = file_path - logger.info(f"Exported {len(annotations)} {annotation_type} annotations to {file_path}") - - return file_paths - - def get_pipeline_stats(self, results: List[Dict]) -> Dict: - """Get statistics about pipeline processing.""" - stats = { - 'total_articles': len(results), - 'successful_articles': 0, - 'relevant_articles': 0, - 'total_annotations': 0, - 'annotations_by_type': {}, - 'validation_pass_rate': 0.0, - 'error_count': 0, - 'errors': [] - } - - for result in results: - if result.get('success'): - stats['successful_articles'] += 1 - - if result.get('relevance', {}).get('is_relevant'): - stats['relevant_articles'] += 1 - - # Count annotations - if result.get('annotations'): - stats['total_annotations'] += len(result['annotations']) - - for annotation in result['annotations']: - ann_type = annotation.get('annotation_type', 'unknown') - stats['annotations_by_type'][ann_type] = stats['annotations_by_type'].get(ann_type, 0) + 1 - - # Validation statistics - if result.get('validation_results'): - valid_count = sum(1 for v in result['validation_results'] if v.get('is_valid')) - total_count = len(result['validation_results']) - if total_count > 0: - stats['validation_pass_rate'] += valid_count / total_count - - if result.get('errors'): - stats['error_count'] += len(result['errors']) - stats['errors'].extend(result['errors']) - - # Average validation pass rate - if stats['successful_articles'] > 0: - stats['validation_pass_rate'] /= stats['successful_articles'] - - return stats \ No newline at end of file From 53a8c1b839ffaad5a5c27b628c034aed8c9b3f74 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 13:59:02 -0700 Subject: [PATCH 13/28] chore: moved PromptGenerator to prompts --- notebooks/test.ipynb | 3 ++- src/annotation_extraction/components.py | 3 ++- src/annotation_extraction/inference.py | 25 ------------------- src/annotation_extraction/prompts.py | 32 +++++++++++++++++++------ 4 files changed, 29 insertions(+), 34 deletions(-) diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb index 4c08887..e45fa3d 100644 --- a/notebooks/test.ipynb +++ b/notebooks/test.ipynb @@ -52,7 +52,8 @@ "metadata": {}, "outputs": [], "source": [ - "from src.annotation_extraction.inference import SimpleLLM, PromptGenerator\n", + "from src.annotation_extraction.inference import SimpleLLM\n", + "from src.annotation_extraction.prompts import PromptGenerator\n", "from src.annotation_extraction.article_parser import ArticleParser\n", "from typing import List" ] diff --git a/src/annotation_extraction/components.py b/src/annotation_extraction/components.py index 0cf3813..319d158 100644 --- a/src/annotation_extraction/components.py +++ b/src/annotation_extraction/components.py @@ -1,4 +1,5 @@ -from src.annotation_extraction.inference import Variant, VariantList, SimpleLLM, PromptGenerator +from src.annotation_extraction.inference import Variant, VariantList, SimpleLLM +from src.annotation_extraction.prompts import PromptGenerator from src.annotation_extraction.article_parser import ArticleParser from loguru import logger import json diff --git a/src/annotation_extraction/inference.py b/src/annotation_extraction/inference.py index eaff4c7..92aaf0a 100644 --- a/src/annotation_extraction/inference.py +++ b/src/annotation_extraction/inference.py @@ -7,31 +7,6 @@ from pydantic import BaseModel, field_validator from typing import List load_dotenv() - -class PromptGenerator: - def __init__(self, prompt_template: str, replacements: Dict[str, str]): - self.prompt_template = prompt_template - self.replacements = replacements - self.validate_replacements() - self.prompt = self.generate_prompt() - - def validate_replacements(self) -> bool: - """Validate that all replacement keys are present in the prompt template.""" - all_valid = True - for key in self.replacements: - if key not in self.prompt_template: - logger.warning(f"Replacement key {key} not found in prompt template.") - all_valid = False - return all_valid - - def generate_prompt(self) -> str: - return self.prompt_template.format(**self.replacements) - - def get_prompt(self) -> str: - return self.prompt - - def get_prompt_template(self) -> str: - return self.prompt_template class LLMInterface: """LLM interface.""" diff --git a/src/annotation_extraction/prompts.py b/src/annotation_extraction/prompts.py index 1a4db2d..436c2fc 100644 --- a/src/annotation_extraction/prompts.py +++ b/src/annotation_extraction/prompts.py @@ -1,9 +1,27 @@ -class PromptTemplates: - """Simple prompts for the LLM annotation pipeline.""" +from typing import Dict +from loguru import logger + +class PromptGenerator: + def __init__(self, prompt_template: str, replacements: Dict[str, str]): + self.prompt_template = prompt_template + self.replacements = replacements + self.validate_replacements() + self.prompt = self.generate_prompt() - SUMMARIZE_PROMPT = """ -You are a pharmacogenomics expert reviewing biomedical literature. Analyze this article and determine if it contains information about genetic variants and their associations with drug response, metabolism, toxicity, or disease phenotypes. + def validate_replacements(self) -> bool: + """Validate that all replacement keys are present in the prompt template.""" + all_valid = True + for key in self.replacements: + if key not in self.prompt_template: + logger.warning(f"Replacement key {key} not found in prompt template.") + all_valid = False + return all_valid -Article Title: {title} -Article Text: {article_text} -""" + def generate_prompt(self) -> str: + return self.prompt_template.format(**self.replacements) + + def get_prompt(self) -> str: + return self.prompt + + def get_prompt_template(self) -> str: + return self.prompt_template From a5783f8051502f691aab6065c0ee21ca04257fe1 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 14:00:52 -0700 Subject: [PATCH 14/28] chore: renamed articleparser to markdownparser --- notebooks/test.ipynb | 9 ++++++--- src/annotation_extraction/README.md | 2 +- src/annotation_extraction/article_parser.py | 5 +++-- src/annotation_extraction/components.py | 4 ++-- 4 files changed, 12 insertions(+), 8 deletions(-) diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb index e45fa3d..4f4e795 100644 --- a/notebooks/test.ipynb +++ b/notebooks/test.ipynb @@ -54,7 +54,7 @@ "source": [ "from src.annotation_extraction.inference import SimpleLLM\n", "from src.annotation_extraction.prompts import PromptGenerator\n", - "from src.annotation_extraction.article_parser import ArticleParser\n", + "from src.annotation_extraction.article_parser import MarkdownParser\n", "from typing import List" ] }, @@ -75,8 +75,11 @@ } ], "source": [ - "article_title = ArticleParser(pmcid=\"PMC11730665\").parse().title\n", - "article_text = ArticleParser(pmcid=\"PMC11730665\").parse().article_text" + "article_title = MarkdownParser(pmcid=\"PMC11730665\").parse().title\n", + "article_text = MarkdownParser(pmcid=\"PMC11730665\").parse().article_text\n", + "\n", + "print(article_title)\n", + "print(article_text)" ] }, { diff --git a/src/annotation_extraction/README.md b/src/annotation_extraction/README.md index 1b3f493..fa70039 100644 --- a/src/annotation_extraction/README.md +++ b/src/annotation_extraction/README.md @@ -4,7 +4,7 @@ ## Examples ``` model = SimpleLLM() -article_text = ArticleParser(pmcid="PMC11730665").get_article_text() +article_text = MarkdownParser(pmcid="PMC11730665").get_article_text() prompt_generator = PromptGenerator(, {"article_text": article_text}) prompt = prompt_generator.get_prompt() output = model.generate(prompt, response_format=) diff --git a/src/annotation_extraction/article_parser.py b/src/annotation_extraction/article_parser.py index e6049f8..93e5eda 100644 --- a/src/annotation_extraction/article_parser.py +++ b/src/annotation_extraction/article_parser.py @@ -10,8 +10,9 @@ class ArticleInput: pmid: str pmcid: Optional[str] = None -class ArticleParser: - """Convert Markdown article text or PMCID to ArticleInput. +class MarkdownParser: + """ + Convert Markdown article text or PMCID to ArticleInput. Args: text: Optional[str] = None diff --git a/src/annotation_extraction/components.py b/src/annotation_extraction/components.py index 319d158..6093156 100644 --- a/src/annotation_extraction/components.py +++ b/src/annotation_extraction/components.py @@ -1,6 +1,6 @@ from src.annotation_extraction.inference import Variant, VariantList, SimpleLLM from src.annotation_extraction.prompts import PromptGenerator -from src.annotation_extraction.article_parser import ArticleParser +from src.annotation_extraction.article_parser import MarkdownParser from loguru import logger import json @@ -32,7 +32,7 @@ def extract_variants_list(article_text: str = None, pmcid: str = None, model: st logger.warning("Both article_text and PMCID are provided. Using article_text.") if article_text is None: - article_text = ArticleParser(pmcid=pmcid).get_article_text() + article_text = MarkdownParser(pmcid=pmcid).get_article_text() if debug: logger.debug(f"Model: {model}, Temperature: {temperature}") From d527d5ab89a05e51eb30337961fb9c560a4174f7 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 20:19:39 -0700 Subject: [PATCH 15/28] feat: generator and promptgenerator components --- src/annotation_extraction/README.md | 2 +- src/annotation_extraction/components.py | 4 +- .../components/all_variants.py | 4 ++ src/annotation_extraction/inference.py | 16 ++++- src/annotation_extraction/prompts.py | 67 ++++++++++++------- 5 files changed, 64 insertions(+), 29 deletions(-) create mode 100644 src/annotation_extraction/components/all_variants.py diff --git a/src/annotation_extraction/README.md b/src/annotation_extraction/README.md index fa70039..e947196 100644 --- a/src/annotation_extraction/README.md +++ b/src/annotation_extraction/README.md @@ -3,7 +3,7 @@ ## Examples ``` -model = SimpleLLM() +model = Generator() article_text = MarkdownParser(pmcid="PMC11730665").get_article_text() prompt_generator = PromptGenerator(, {"article_text": article_text}) prompt = prompt_generator.get_prompt() diff --git a/src/annotation_extraction/components.py b/src/annotation_extraction/components.py index 6093156..dd442fe 100644 --- a/src/annotation_extraction/components.py +++ b/src/annotation_extraction/components.py @@ -1,4 +1,4 @@ -from src.annotation_extraction.inference import Variant, VariantList, SimpleLLM +from src.annotation_extraction.inference import Variant, VariantList, Generator from src.annotation_extraction.prompts import PromptGenerator from src.annotation_extraction.article_parser import MarkdownParser from loguru import logger @@ -38,7 +38,7 @@ def extract_variants_list(article_text: str = None, pmcid: str = None, model: st logger.debug(f"Model: {model}, Temperature: {temperature}") logger.debug(f"PMCID: {pmcid}") - model = SimpleLLM(model=model, temperature=temperature) + model = Generator(model=model, temperature=temperature) prompt_generator = PromptGenerator(VARIANT_LIST_PROMPT, {"article_text": article_text}) prompt = prompt_generator.get_prompt() output = model.generate(prompt, response_format=VariantList) diff --git a/src/annotation_extraction/components/all_variants.py b/src/annotation_extraction/components/all_variants.py new file mode 100644 index 0000000..d7ec0c8 --- /dev/null +++ b/src/annotation_extraction/components/all_variants.py @@ -0,0 +1,4 @@ +""" +This module is used to extract all variants from an article. +Markdown --> All relevant variants +""" \ No newline at end of file diff --git a/src/annotation_extraction/inference.py b/src/annotation_extraction/inference.py index 92aaf0a..0f1c685 100644 --- a/src/annotation_extraction/inference.py +++ b/src/annotation_extraction/inference.py @@ -17,7 +17,7 @@ def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): def generate(self, prompt: str, temperature: float = None) -> str: raise NotImplementedError("Subclasses must implement this method.") -class SimpleLLM(LLMInterface): +class Generator(LLMInterface): """Simple LLM interface that just returns as response to the prompt.""" debug_mode = False @@ -28,9 +28,19 @@ def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): if self.debug_mode: litellm.set_verbose = True - def generate(self, prompt: str, temperature: float = None, response_format: BaseModel = None) -> str: + def generate(self, prompt: str, system_prompt: str = None, temperature: float = None, response_format: BaseModel = None) -> str: temp = temperature if temperature is not None else self.temperature - response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], response_format=response_format, temperature=temp) + # Check if system prompt is provided + if system_prompt is not None and system_prompt != "": + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt} + ] + else: + messages = [ + {"role": "user", "content": prompt} + ] + response = litellm.completion(model=self.model, messages=messages, response_format=response_format, temperature=temp) return response.choices[0].message.content class Variant(BaseModel): diff --git a/src/annotation_extraction/prompts.py b/src/annotation_extraction/prompts.py index 436c2fc..89a8d14 100644 --- a/src/annotation_extraction/prompts.py +++ b/src/annotation_extraction/prompts.py @@ -1,27 +1,48 @@ -from typing import Dict +from typing import Dict, Optional, Type from loguru import logger +from pydantic import BaseModel +""" +This module is used to generate prompts for the LLM. +The variables that go into the prompt template are: +- system prompt +- article text +- key question +- output queues +- output format +""" + +PROMPT_TEMPLATE = """ +You are an expert pharmacogenomics researcher reading and extracting key information from the following article: + +{article_text} + +{key_question} + +{output_queues} +""" +class PromptVariables(BaseModel): + """Input variables for prompt generation.""" + article_text: str + key_question: str + output_queues: Optional[str] = None + system_prompt: Optional[str] = None + output_format_structure: Optional[Type[BaseModel]] = None + +class HydratedPrompt(BaseModel): + """Final prompt with system and input components.""" + system_prompt: Optional[str] = None + input_prompt: str + output_format_structure: Optional[Type[BaseModel]] = None class PromptGenerator: - def __init__(self, prompt_template: str, replacements: Dict[str, str]): - self.prompt_template = prompt_template - self.replacements = replacements - self.validate_replacements() - self.prompt = self.generate_prompt() - - def validate_replacements(self) -> bool: - """Validate that all replacement keys are present in the prompt template.""" - all_valid = True - for key in self.replacements: - if key not in self.prompt_template: - logger.warning(f"Replacement key {key} not found in prompt template.") - all_valid = False - return all_valid + def __init__(self, prompt_variables: PromptVariables): + self.prompt_template = PROMPT_TEMPLATE + self.prompt_variables = prompt_variables - def generate_prompt(self) -> str: - return self.prompt_template.format(**self.replacements) - - def get_prompt(self) -> str: - return self.prompt - - def get_prompt_template(self) -> str: - return self.prompt_template + def hydrate_prompt(self) -> HydratedPrompt: + """Hydrate the prompt.""" + return HydratedPrompt( + system_prompt=self.prompt_variables.system_prompt, + input_prompt=self.prompt_template.format(**self.prompt_variables), + output_format_structure=self.prompt_variables.output_format_structure + ) From 2cbc23c997440b7ab3fdec753af223f84b714305 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 20:21:54 -0700 Subject: [PATCH 16/28] feat: moved files out of annotation_extraction --- src/{annotation_extraction => }/README.md | 0 src/__init__.py | 10 +++++++--- src/annotation_extraction/__init__.py | 7 ------- src/annotation_extraction/components/all_variants.py | 4 ---- src/{annotation_extraction => }/article_parser.py | 0 .../components.py => components/all_variants.py} | 0 src/{annotation_extraction => }/inference.py | 0 src/{annotation_extraction => }/prompts.py | 0 src/{annotation_extraction => }/utils.py | 0 9 files changed, 7 insertions(+), 14 deletions(-) rename src/{annotation_extraction => }/README.md (100%) delete mode 100644 src/annotation_extraction/__init__.py delete mode 100644 src/annotation_extraction/components/all_variants.py rename src/{annotation_extraction => }/article_parser.py (100%) rename src/{annotation_extraction/components.py => components/all_variants.py} (100%) rename src/{annotation_extraction => }/inference.py (100%) rename src/{annotation_extraction => }/prompts.py (100%) rename src/{annotation_extraction => }/utils.py (100%) diff --git a/src/annotation_extraction/README.md b/src/README.md similarity index 100% rename from src/annotation_extraction/README.md rename to src/README.md diff --git a/src/__init__.py b/src/__init__.py index 60e6ed9..bc091a9 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -1,3 +1,7 @@ -# SPDX-FileCopyrightText: 2025 Stanford University and the project authors (see CONTRIBUTORS.md) -# -# SPDX-License-Identifier: Apache-2.0 +""" +Annotation extraction module for pharmacogenomic variant annotations. + +This module implements a multi-stage LLM pipeline for extracting structured +pharmacogenomic variant annotations from biomedical articles. +""" + diff --git a/src/annotation_extraction/__init__.py b/src/annotation_extraction/__init__.py deleted file mode 100644 index bc091a9..0000000 --- a/src/annotation_extraction/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Annotation extraction module for pharmacogenomic variant annotations. - -This module implements a multi-stage LLM pipeline for extracting structured -pharmacogenomic variant annotations from biomedical articles. -""" - diff --git a/src/annotation_extraction/components/all_variants.py b/src/annotation_extraction/components/all_variants.py deleted file mode 100644 index d7ec0c8..0000000 --- a/src/annotation_extraction/components/all_variants.py +++ /dev/null @@ -1,4 +0,0 @@ -""" -This module is used to extract all variants from an article. -Markdown --> All relevant variants -""" \ No newline at end of file diff --git a/src/annotation_extraction/article_parser.py b/src/article_parser.py similarity index 100% rename from src/annotation_extraction/article_parser.py rename to src/article_parser.py diff --git a/src/annotation_extraction/components.py b/src/components/all_variants.py similarity index 100% rename from src/annotation_extraction/components.py rename to src/components/all_variants.py diff --git a/src/annotation_extraction/inference.py b/src/inference.py similarity index 100% rename from src/annotation_extraction/inference.py rename to src/inference.py diff --git a/src/annotation_extraction/prompts.py b/src/prompts.py similarity index 100% rename from src/annotation_extraction/prompts.py rename to src/prompts.py diff --git a/src/annotation_extraction/utils.py b/src/utils.py similarity index 100% rename from src/annotation_extraction/utils.py rename to src/utils.py From f1005060bf06ff78463c3891434f93270a96acd0 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 20:22:48 -0700 Subject: [PATCH 17/28] fix: imports --- src/components/all_variants.py | 6 +++--- src/utils.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/components/all_variants.py b/src/components/all_variants.py index dd442fe..34329c1 100644 --- a/src/components/all_variants.py +++ b/src/components/all_variants.py @@ -1,6 +1,6 @@ -from src.annotation_extraction.inference import Variant, VariantList, Generator -from src.annotation_extraction.prompts import PromptGenerator -from src.annotation_extraction.article_parser import MarkdownParser +from src.inference import Variant, VariantList, Generator +from src.prompts import PromptGenerator +from src.article_parser import MarkdownParser from loguru import logger import json diff --git a/src/utils.py b/src/utils.py index 19056ab..c8e7e8c 100644 --- a/src/utils.py +++ b/src/utils.py @@ -2,7 +2,7 @@ from termcolor import colored from loguru import logger -def extractVariants(text): +def extractVariantsRegex(text): # Note, seems to extract a ton of variants, not just the ones that are being studied # Think it might only be applicable to rsIDs variantRegex = r'\b([A-Z]+\d+[A-Z]*\*\d+|\brs\d+)\b' From 9540d9734558867f1a24bf4e870997d1835f323e Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 20:23:51 -0700 Subject: [PATCH 18/28] chore: black formatting --- src/__init__.py | 1 - src/article_parser.py | 44 ++++++++++++++---------- src/components/all_variants.py | 17 +++++++--- src/inference.py | 62 ++++++++++++++++++++++++++-------- src/prompts.py | 9 ++++- src/utils.py | 6 ++-- 6 files changed, 100 insertions(+), 39 deletions(-) diff --git a/src/__init__.py b/src/__init__.py index bc091a9..413ae21 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -4,4 +4,3 @@ This module implements a multi-stage LLM pipeline for extracting structured pharmacogenomic variant annotations from biomedical articles. """ - diff --git a/src/article_parser.py b/src/article_parser.py index 93e5eda..35ea9ab 100644 --- a/src/article_parser.py +++ b/src/article_parser.py @@ -2,24 +2,34 @@ from typing import Optional from dataclasses import dataclass from pathlib import Path + + @dataclass class ArticleInput: """Input article data.""" + title: str article_text: str pmid: str pmcid: Optional[str] = None + class MarkdownParser: """ Convert Markdown article text or PMCID to ArticleInput. - + Args: text: Optional[str] = None pmcid: Optional[str] = None remove_references: bool = True """ - def __init__(self, text: Optional[str] = None, pmcid: Optional[str] = None, remove_references: bool = True): + + def __init__( + self, + text: Optional[str] = None, + pmcid: Optional[str] = None, + remove_references: bool = True, + ): self.text = text self.pmcid = pmcid self.remove_references = remove_references @@ -34,7 +44,7 @@ def __init__(self, text: Optional[str] = None, pmcid: Optional[str] = None, remo self.text = self.get_article_text() if self.remove_references: self.remove_references_section() - + def get_article_text(self) -> str: """Get article text from PMCID.""" article_path = Path("data") / "articles" / f"{self.pmcid}.md" @@ -43,7 +53,7 @@ def get_article_text(self) -> str: raise FileNotFoundError(f"Article not found: {article_path}") with open(article_path, "r", encoding="utf-8") as f: return f.read() - + def parse_title(self) -> str: """Parse the title from the markdown text.""" lines = self.text.split("\n") @@ -53,58 +63,58 @@ def parse_title(self) -> str: if title.startswith("# "): title = title[2:].strip() return title - + def remove_references_section(self): """ Removes the references section from article text. - + Returns: str: Article text with references section removed (Looks for ## References section and removes it and everything after) """ # Split the text into sections sections = self.text.split("##") - + # Find the index of the References section ref_index = -1 for i, section in enumerate(sections): if section.strip().startswith("References"): ref_index = i break - + # If references section found, remove it and everything after if ref_index != -1: sections = sections[:ref_index] self.text = "##".join(sections) - + logger.info(f"Removed References section from article text") - + def parse_pmid(self) -> str: """Parse the PMID from the markdown text.""" lines = self.text.split("\n") - + # Look for PMID in metadata section for line in lines: if line.strip().startswith("**PMID:**"): pmid = line.replace("**PMID:**", "").strip() return pmid - + return "" - + def parse_pmcid(self) -> str: """Parse the PMCID from the markdown text.""" if self.pmcid: return self.pmcid lines = self.text.split("\n") - + # Look for PMCID in metadata section for line in lines: if line.strip().startswith("**PMCID:**"): pmcid = line.replace("**PMCID:**", "").strip() return pmcid - + return "" - + def parse(self) -> ArticleInput: """Parse the article text into an ArticleInput.""" return ArticleInput( @@ -112,4 +122,4 @@ def parse(self) -> ArticleInput: article_text=self.text, pmid=self.parse_pmid(), pmcid=self.parse_pmcid(), - ) \ No newline at end of file + ) diff --git a/src/components/all_variants.py b/src/components/all_variants.py index 34329c1..4b5aa17 100644 --- a/src/components/all_variants.py +++ b/src/components/all_variants.py @@ -15,7 +15,14 @@ Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.) """ -def extract_variants_list(article_text: str = None, pmcid: str = None, model: str = "gpt-4o", temperature: float = 0.1, debug: bool = False) -> VariantList: + +def extract_variants_list( + article_text: str = None, + pmcid: str = None, + model: str = "gpt-4o", + temperature: float = 0.1, + debug: bool = False, +) -> VariantList: """Extract a list of variants from an article. Args: article_text: The text of the article. @@ -27,7 +34,7 @@ def extract_variants_list(article_text: str = None, pmcid: str = None, model: st if article_text is None and pmcid is None: logger.error("Either article_text or pmcid must be provided.") raise ValueError("Either article_text or pmcid must be provided.") - + if article_text is not None and pmcid is not None: logger.warning("Both article_text and PMCID are provided. Using article_text.") @@ -39,9 +46,11 @@ def extract_variants_list(article_text: str = None, pmcid: str = None, model: st logger.debug(f"PMCID: {pmcid}") model = Generator(model=model, temperature=temperature) - prompt_generator = PromptGenerator(VARIANT_LIST_PROMPT, {"article_text": article_text}) + prompt_generator = PromptGenerator( + VARIANT_LIST_PROMPT, {"article_text": article_text} + ) prompt = prompt_generator.get_prompt() output = model.generate(prompt, response_format=VariantList) - variant_list = json.loads(output)['variant_list'] + variant_list = json.loads(output)["variant_list"] # variant_list_strs = [i['variant_id'] for i in variant_list] return variant_list diff --git a/src/inference.py b/src/inference.py index 0f1c685..b944486 100644 --- a/src/inference.py +++ b/src/inference.py @@ -6,10 +6,13 @@ from dotenv import load_dotenv from pydantic import BaseModel, field_validator from typing import List + load_dotenv() - + + class LLMInterface: """LLM interface.""" + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): self.model = model self.temperature = temperature @@ -17,92 +20,123 @@ def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): def generate(self, prompt: str, temperature: float = None) -> str: raise NotImplementedError("Subclasses must implement this method.") + class Generator(LLMInterface): """Simple LLM interface that just returns as response to the prompt.""" + debug_mode = False - + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): self.model = model self.temperature = temperature - + if self.debug_mode: litellm.set_verbose = True - - def generate(self, prompt: str, system_prompt: str = None, temperature: float = None, response_format: BaseModel = None) -> str: + + def generate( + self, + prompt: str, + system_prompt: str = None, + temperature: float = None, + response_format: BaseModel = None, + ) -> str: temp = temperature if temperature is not None else self.temperature # Check if system prompt is provided if system_prompt is not None and system_prompt != "": messages = [ {"role": "system", "content": system_prompt}, - {"role": "user", "content": prompt} + {"role": "user", "content": prompt}, ] else: - messages = [ - {"role": "user", "content": prompt} - ] - response = litellm.completion(model=self.model, messages=messages, response_format=response_format, temperature=temp) + messages = [{"role": "user", "content": prompt}] + response = litellm.completion( + model=self.model, + messages=messages, + response_format=response_format, + temperature=temp, + ) return response.choices[0].message.content + class Variant(BaseModel): """Variant.""" + variant_id: str gene: str | None = None allele: str | None = None # TODO: Add field validation for gene and allele + class VariantList(BaseModel): """List of variants.""" + variant_list: List[Variant] + class StrEntry(BaseModel): """String entry.""" + text: str reason: str quote: str + class EnumEntry(BaseModel): """Enum entry.""" + options: List[str] text: str reason: str quote: str - @field_validator('text') + @field_validator("text") @classmethod def validate_text_in_options(cls, v: str, info) -> str: - if 'options' in info.data and v not in info.data['options']: + if "options" in info.data and v not in info.data["options"]: raise ValueError(f"Text {v} not in options {info.data['options']}") return v class VarDrugResponse(BaseModel): """Response from VarDrug.""" + variant_id: StrEntry drug_id: StrEntry effect: StrEntry evidence: StrEntry + class VarPhenoResponse(BaseModel): """Response from VarPheno.""" + variant_id: StrEntry phenotype: StrEntry evidence: StrEntry + class VarFAResponse(BaseModel): """Response from VarFA.""" + variant_id: StrEntry phenotype: StrEntry evidence: StrEntry + class VarDrugParser(LLMInterface): """LLM interface that parses a VarDrug response into a dictionary.""" + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): super().__init__(model, temperature) if self.debug_mode: litellm.set_verbose = True - + def generate(self, prompt: str, temperature: float = None) -> str: temp = temperature if temperature is not None else self.temperature - response = litellm.completion(model=self.model, messages=[{"role": "user", "content": prompt}], structured_output=True, temperature=temp) + response = litellm.completion( + model=self.model, + messages=[{"role": "user", "content": prompt}], + structured_output=True, + temperature=temp, + ) return response.choices[0].message.content diff --git a/src/prompts.py b/src/prompts.py index 89a8d14..6fdcd23 100644 --- a/src/prompts.py +++ b/src/prompts.py @@ -1,6 +1,7 @@ from typing import Dict, Optional, Type from loguru import logger from pydantic import BaseModel + """ This module is used to generate prompts for the LLM. The variables that go into the prompt template are: @@ -20,20 +21,26 @@ {output_queues} """ + + class PromptVariables(BaseModel): """Input variables for prompt generation.""" + article_text: str key_question: str output_queues: Optional[str] = None system_prompt: Optional[str] = None output_format_structure: Optional[Type[BaseModel]] = None + class HydratedPrompt(BaseModel): """Final prompt with system and input components.""" + system_prompt: Optional[str] = None input_prompt: str output_format_structure: Optional[Type[BaseModel]] = None + class PromptGenerator: def __init__(self, prompt_variables: PromptVariables): self.prompt_template = PROMPT_TEMPLATE @@ -44,5 +51,5 @@ def hydrate_prompt(self) -> HydratedPrompt: return HydratedPrompt( system_prompt=self.prompt_variables.system_prompt, input_prompt=self.prompt_template.format(**self.prompt_variables), - output_format_structure=self.prompt_variables.output_format_structure + output_format_structure=self.prompt_variables.output_format_structure, ) diff --git a/src/utils.py b/src/utils.py index c8e7e8c..d7cfaa5 100644 --- a/src/utils.py +++ b/src/utils.py @@ -2,12 +2,14 @@ from termcolor import colored from loguru import logger + def extractVariantsRegex(text): # Note, seems to extract a ton of variants, not just the ones that are being studied # Think it might only be applicable to rsIDs - variantRegex = r'\b([A-Z]+\d+[A-Z]*\*\d+|\brs\d+)\b' + variantRegex = r"\b([A-Z]+\d+[A-Z]*\*\d+|\brs\d+)\b" return re.findall(variantRegex, text) or [] + def save_output(prompt, output, filename): # save prompt and output to file with open(f"test_outputs/{filename}.txt", "w") as f: @@ -15,4 +17,4 @@ def save_output(prompt, output, filename): f.write(prompt) f.write("\nOutput:\n") f.write(output) - logger.info(f"Saved output to {filename}.txt") \ No newline at end of file + logger.info(f"Saved output to {filename}.txt") From 21292bb7a6adf8a7241b57df6054ce5fb1686bc0 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 22:04:43 -0700 Subject: [PATCH 19/28] feat: working all variants --- notebooks/all_variants.ipynb | 144 +++++++++++++++++++++++++++++++++ src/components/all_variants.py | 70 ++++++++++++---- src/inference.py | 17 ++-- src/prompts.py | 2 +- 4 files changed, 212 insertions(+), 21 deletions(-) create mode 100644 notebooks/all_variants.ipynb diff --git a/notebooks/all_variants.ipynb b/notebooks/all_variants.ipynb new file mode 100644 index 0000000..3605950 --- /dev/null +++ b/notebooks/all_variants.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# Run this cell: \n", + "# The lines below will instruct jupyter to reload imported modules before \n", + "# executing code cells. This enables you to quickly iterate and test revisions\n", + "# to your code without having to restart the kernel and reload all of your \n", + "# modules each time you make a code change in a separate python file.\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current working directory: /Users/shloknatarajan/stanford/research/daneshjou/AutoGKB\n" + ] + } + ], + "source": [ + "import os\n", + "# Change path to project root\n", + "if os.getcwd().endswith(\"notebooks\"):\n", + " os.chdir(os.path.dirname(os.getcwd()))\n", + "print(f\"Current working directory: {os.getcwd()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 22:03:42.333\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 22:03:42.334\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 22:03:42.335\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n" + ] + } + ], + "source": [ + "from src.components.all_variants import extract_all_variants\n", + "\n", + "variants = extract_all_variants(pmcid=\"PMC11730665\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Variant(variant_id='rs2909451', gene='DPP4', allele='TT genotype (lower efficacy with sitagliptin) (Evidence: \"Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.\")'),\n", + " Variant(variant_id='rs4664443', gene='DPP4', allele='GG genotype (lower efficacy with sitagliptin) (Evidence: \"Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.\")'),\n", + " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG genotype (better response to sitagliptin) (Evidence: \"Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.\")'),\n", + " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC genotype (better response to sitagliptin) (Evidence: \"Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.\")'),\n", + " Variant(variant_id='rs163184', gene='KCNQ1', allele='GG allele (lower responsiveness to sitagliptin) (Evidence: \"KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group, suggesting lower responsiveness to sitagliptin and better response to gliclazide.\")'),\n", + " Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG genotype (more significant HbA1c reductions with sitagliptin) (Evidence: \"CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group.\")'),\n", + " Variant(variant_id='rs756992', gene='CDKAL1', allele='AG genotype (more significant HbA1c reductions with sitagliptin) (Evidence: \"CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group.\")'),\n", + " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT genotype (slower metabolism of gliclazide) (Evidence: \"CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).\")'),\n", + " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG genotype (slower metabolism of gliclazide) (Evidence: \"For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group.\")')]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variants" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test to see if variants are now Variant objects\n", + "print(f\"Type of first variant: {type(variants[0])}\")\n", + "print(f\"First variant: {variants[0]}\")\n", + "if hasattr(variants[0], 'evidence'):\n", + " print(f\"Evidence field: {variants[0].evidence}\")\n", + "else:\n", + " print(\"No evidence attribute found\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "variants_debug = extract_all_variants(pmcid=\"PMC11730665\", debug=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "default", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/components/all_variants.py b/src/components/all_variants.py index 4b5aa17..83ba09a 100644 --- a/src/components/all_variants.py +++ b/src/components/all_variants.py @@ -1,28 +1,31 @@ from src.inference import Variant, VariantList, Generator -from src.prompts import PromptGenerator +from src.prompts import PromptGenerator, PromptVariables from src.article_parser import MarkdownParser from loguru import logger import json +from typing import List -VARIANT_LIST_PROMPT = """ -You are an expert pharmacogenomics researcher reading and extracting annotations from the following article: - -{article_text} +VARIANT_LIST_KEY_QUESTION = """From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.). Include information on the gene group and allele (if present). +Make sure they variant has a studied association (likely discussed in the methodology or results section), not simply mentioned as background information. +""" -From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.). Include information on the gene group and allele (if present). Your output format should be a list of the variants with the following attributes: +VARIANT_LIST_OUTPUT_QUEUES = """Your output format should be a list of the variants with the following attributes: Variant: The Variant / Haplotypes (ex. rs2909451, CYP2C19*1, CYP2C19*2, *1/*18, etc.) Gene: The gene group of the variant (ex. DPP4, CYP2C19, KCNJ11, etc.) Allele: Specific allele or genotype if different from variant (ex. TT, *1/*18, del/del, etc.) +Evidence: REQUIRED - A direct quote from the article that mentions this specific variant. Find the exact text where this variant is discussed in the methodology or results section. + +IMPORTANT: You MUST include the evidence field for every variant. Do not leave it empty or null. """ -def extract_variants_list( +def extract_all_variants( article_text: str = None, pmcid: str = None, model: str = "gpt-4o", temperature: float = 0.1, debug: bool = False, -) -> VariantList: +) -> List[Variant]: """Extract a list of variants from an article. Args: article_text: The text of the article. @@ -46,11 +49,50 @@ def extract_variants_list( logger.debug(f"PMCID: {pmcid}") model = Generator(model=model, temperature=temperature) - prompt_generator = PromptGenerator( - VARIANT_LIST_PROMPT, {"article_text": article_text} + prompt_variables = PromptVariables( + article_text=article_text, + key_question=VARIANT_LIST_KEY_QUESTION, + output_queues=VARIANT_LIST_OUTPUT_QUEUES, + output_format_structure=VariantList ) - prompt = prompt_generator.get_prompt() - output = model.generate(prompt, response_format=VariantList) - variant_list = json.loads(output)["variant_list"] - # variant_list_strs = [i['variant_id'] for i in variant_list] + prompt_generator = PromptGenerator(prompt_variables) + hydrated_prompt = prompt_generator.hydrate_prompt() + logger.info(f"Extracting all variants") + output = model.generate(hydrated_prompt.input_prompt, response_format=VariantList) + if debug: + logger.debug(f"Raw LLM output: {output}") + parsed_output = json.loads(output) + if debug: + logger.debug(f"Parsed output: {parsed_output}") + variant_list = [Variant(**variant_data) for variant_data in parsed_output["variant_list"]] + logger.info(f"Found {len(variant_list)} variants") return variant_list + +def main(pmcid: str, model: str = "gpt-4o", temperature: float = 0.1, output: str = None): + """Main function to demonstrate variant extraction functionality.""" + try: + # Extract variants + variants = extract_all_variants( + pmcid=pmcid, + model=model, + temperature=temperature + ) + + # Print results + print(f"Found {len(variants)} variants:") + for i, variant in enumerate(variants, 1): + print(f"{i}. Variant: {variant.variant_id}") + print(f" Gene: {variant.gene}") + print(f" Allele: {variant.allele}") + print(f" Evidence: {variant.evidence}") + print() + + # Save to file if output path specified + if output: + with open(output, 'w') as f: + json.dump({"variants": variants}, f, indent=2) + print(f"Results saved to {output}") + + except Exception as e: + logger.error(f"Error extracting variants: {e}") + raise \ No newline at end of file diff --git a/src/inference.py b/src/inference.py index b944486..69469a5 100644 --- a/src/inference.py +++ b/src/inference.py @@ -49,12 +49,16 @@ def generate( ] else: messages = [{"role": "user", "content": prompt}] - response = litellm.completion( - model=self.model, - messages=messages, - response_format=response_format, - temperature=temp, - ) + try: + response = litellm.completion( + model=self.model, + messages=messages, + response_format=response_format, + temperature=temp, + ) + except Exception as e: + logger.error(f"Error generating response: {e}") + raise e return response.choices[0].message.content @@ -64,6 +68,7 @@ class Variant(BaseModel): variant_id: str gene: str | None = None allele: str | None = None + evidence: str | None = None # TODO: Add field validation for gene and allele diff --git a/src/prompts.py b/src/prompts.py index 6fdcd23..4056822 100644 --- a/src/prompts.py +++ b/src/prompts.py @@ -50,6 +50,6 @@ def hydrate_prompt(self) -> HydratedPrompt: """Hydrate the prompt.""" return HydratedPrompt( system_prompt=self.prompt_variables.system_prompt, - input_prompt=self.prompt_template.format(**self.prompt_variables), + input_prompt=self.prompt_template.format(**self.prompt_variables.model_dump()), output_format_structure=self.prompt_variables.output_format_structure, ) From 76c82c4af04c8b2286d2d65254bd0f224e51c29b Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 22:14:25 -0700 Subject: [PATCH 20/28] feat: get ground truth variant list --- data/benchmark/true_variant_list.json | 57185 +++++++++++++++++ notebooks/all_variants.ipynb | 74 +- notebooks/{test.ipynb => intermediate.ipynb} | 16 +- src/utils.py | 63 +- tests/utils.py | 52 - tests/variant_list_tests.py | 7 +- 6 files changed, 57316 insertions(+), 81 deletions(-) create mode 100644 data/benchmark/true_variant_list.json rename notebooks/{test.ipynb => intermediate.ipynb} (99%) delete mode 100644 tests/utils.py diff --git a/data/benchmark/true_variant_list.json b/data/benchmark/true_variant_list.json new file mode 100644 index 0000000..8ba4e02 --- /dev/null +++ b/data/benchmark/true_variant_list.json @@ -0,0 +1,57185 @@ +{ + "PMC5712579": [ + { + "variant_id": "HLA-B*35:08", + "gene": "HLA-B", + "allele": "*35:08" + }, + { + "variant_id": "HLA-B*39:01", + "gene": "HLA-B", + "allele": "*39:01" + }, + { + "variant_id": "HLA-B*15:02", + "gene": "HLA-B", + "allele": "*15:02" + }, + { + "variant_id": "HLA-B*44:03", + "gene": "HLA-B", + "allele": "*44:03" + }, + { + "variant_id": "HLA-A*02:07", + "gene": "HLA-A", + "allele": "*02:07" + }, + { + "variant_id": "HLA-A*33:03", + "gene": "HLA-A", + "allele": "*33:03" + } + ], + "PMC3202555": [ + { + "variant_id": "rs1801272", + "gene": "CYP2A6", + "allele": "AA + AT" + }, + { + "variant_id": "rs1801272", + "gene": "CYP2A6", + "allele": "AT + TT" + }, + { + "variant_id": "CYP2A6*1, CYP2A6*12", + "gene": "CYP2A6", + "allele": "*12" + }, + { + "variant_id": "CYP2A6*1, CYP2A6*1x2", + "gene": "CYP2A6", + "allele": "*1x2" + }, + { + "variant_id": "CYP2B6*1, CYP2B6*9", + "gene": "CYP2B6", + "allele": "*9" + }, + { + "variant_id": "rs28399433", + "gene": "CYP2A6", + "allele": "A" + }, + { + "variant_id": "CYP2B6*1, CYP2B6*4", + "gene": "CYP2B6", + "allele": "*4" + }, + { + "variant_id": "rs8192789", + "gene": "CYP2A13", + "allele": "T" + }, + { + "variant_id": "rs1709083", + "gene": "CYP2A13", + "allele": "G" + }, + { + "variant_id": "CYP2B6*1, CYP2B6*4", + "gene": "CYP2B6", + "allele": "*1/*4 + *4/*4" + }, + { + "variant_id": "CYP2A6*1, CYP2A6*12", + "gene": "CYP2A6", + "allele": "*1/*1" + }, + { + "variant_id": "CYP2B6*1, CYP2B6*9", + "gene": "CYP2B6", + "allele": "*9" + }, + { + "variant_id": "rs72552266", + "gene": "CYP2A13", + "allele": "T" + }, + { + "variant_id": "rs578776", + "gene": "CHRNA3", + "allele": "A" + }, + { + "variant_id": "CYP2A6*1, CYP2A6*1x2", + "gene": "CYP2A6", + "allele": "*1/*1x2" + }, + { + "variant_id": "rs6311", + "gene": "HTR2A", + "allele": "C" + }, + { + "variant_id": "rs6928499", + "gene": "CNR1", + "allele": "G" + }, + { + "variant_id": "rs72552266", + "gene": "CYP2A13", + "allele": "CT" + }, + { + "variant_id": "rs8192789", + "gene": "CYP2A13", + "allele": "T" + }, + { + "variant_id": "rs16969968", + "gene": "CHRNA5", + "allele": "A" + }, + { + "variant_id": "rs1709083", + "gene": "CYP2A13", + "allele": "CG" + }, + { + "variant_id": "rs1799971", + "gene": "OPRM1", + "allele": "G" + }, + { + "variant_id": "rs1800497", + "gene": "ANKK1, DRD2", + "allele": "AA + AG" + }, + { + "variant_id": "rs28399433", + "gene": "CYP2A6", + "allele": "CC" + }, + { + "variant_id": "rs6311", + "gene": "HTR2A", + "allele": "C" + }, + { + "variant_id": "rs148044792", + "gene": "CYP2A13", + "allele": "A" + }, + { + "variant_id": "rs1799971", + "gene": "OPRM1", + "allele": "G" + }, + { + "variant_id": "rs1800497", + "gene": "ANKK1, DRD2", + "allele": "A" + }, + { + "variant_id": "rs6928499", + "gene": "CNR1", + "allele": "G" + }, + { + "variant_id": "rs148044792", + "gene": "CYP2A13", + "allele": "A" + }, + { + "variant_id": "rs16969968", + "gene": "CHRNA5", + "allele": "A" + }, + { + "variant_id": "rs578776", + "gene": "CHRNA3", + "allele": "A" + } + ], + "PMC11554802": [ + { + "variant_id": "UGT1A1*1, UGT1A1*28", + "gene": "UGT1A1", + "allele": "*1/*28 + *28/*28" + } + ], + "PMC9261480": [ + { + "variant_id": "ABCB1 intermediate activity", + "gene": "ABCB1", + "allele": null + }, + { + "variant_id": "CYP3A4 poor metabolizer", + "gene": "CYP3A4", + "allele": null + } + ], + "PMC11954185": [ + { + "variant_id": "HLA-A*02:01", + "gene": "HLA-A", + "allele": "*02:01" + } + ], + "PMC11936550": [ + { + "variant_id": "rs2279343", + "gene": "CYP2B6", + "allele": "AG + GG" + }, + { + "variant_id": "rs2279344", + "gene": "CYP2B6", + "allele": "AA + AG" + } + ], + "PMC6016699": [ + { + "variant_id": "rs804290", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs4840579", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs17153694", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs867858", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs11250159", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs12550668", + "gene": "GATA4", + "allele": "A" + }, + { + "variant_id": "rs2898292", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs6990313", + "gene": "GATA4", + "allele": "T" + }, + { + "variant_id": "rs10105409", + "gene": "GATA4", + "allele": "C" + }, + { + "variant_id": "rs6601604", + "gene": "GATA4", + 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"variant_id": "rs1042028", + "gene": "SULT1A1", + "allele": "T" + } + ], + "PMC3602211": [ + { + "variant_id": "rs1801158", + "gene": "DPYD", + "allele": "TT" + }, + { + "variant_id": "rs1801159", + "gene": "DPYD", + "allele": "CC" + }, + { + "variant_id": "rs1801265", + "gene": "DPYD", + "allele": "GG" + }, + { + "variant_id": "rs55886062", + "gene": "DPYD", + "allele": "CC" + }, + { + "variant_id": "rs1801160", + "gene": "DPYD", + "allele": "TT" + }, + { + "variant_id": "rs3918290", + "gene": "DPYD", + "allele": "TT" + } + ] +} \ No newline at end of file diff --git a/notebooks/all_variants.ipynb b/notebooks/all_variants.ipynb index 3605950..641e769 100644 --- a/notebooks/all_variants.ipynb +++ b/notebooks/all_variants.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -48,16 +48,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 22:03:42.333\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 22:03:42.334\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 22:03:42.335\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n" + "\u001b[32m2025-06-26 22:06:23.108\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 22:06:23.110\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 22:06:23.111\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 22:06:35.703\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mFound 6 variants\u001b[0m\n" ] } ], @@ -69,24 +76,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[Variant(variant_id='rs2909451', gene='DPP4', allele='TT genotype (lower efficacy with sitagliptin) (Evidence: \"Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.\")'),\n", - " Variant(variant_id='rs4664443', gene='DPP4', allele='GG genotype (lower efficacy with sitagliptin) (Evidence: \"Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.\")'),\n", - " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG genotype (better response to sitagliptin) (Evidence: \"Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.\")'),\n", - " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC genotype (better response to sitagliptin) (Evidence: \"Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.\")'),\n", - " Variant(variant_id='rs163184', gene='KCNQ1', allele='GG allele (lower responsiveness to sitagliptin) (Evidence: \"KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group, suggesting lower responsiveness to sitagliptin and better response to gliclazide.\")'),\n", - " Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG genotype (more significant HbA1c reductions with sitagliptin) (Evidence: \"CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group.\")'),\n", - " Variant(variant_id='rs756992', gene='CDKAL1', allele='AG genotype (more significant HbA1c reductions with sitagliptin) (Evidence: \"CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group.\")'),\n", - " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT genotype (slower metabolism of gliclazide) (Evidence: \"CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).\")'),\n", - " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG genotype (slower metabolism of gliclazide) (Evidence: \"For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group.\")')]" + "[Variant(variant_id='rs2909451', gene='DPP4', allele='TT genotype (lower efficacy) with sitagliptin treatment in the study group compared to the control group (treated with gliclazide). \"Patients with the rs2909451 TT genotype in the study group (treated with sitagliptin) exhibited a median HbA1c improvement of 0.57 (interquartile range [IQR], 0.18–0.85), whereas the control group (treated with gliclazide) showed a median improvement of 1.11 (IQR, 0.86–1.35; *P* < .001).\"'),\n", + " Variant(variant_id='rs4664443', gene='DPP4', allele='GG genotype (lower efficacy) with sitagliptin treatment in the study group compared to the control group (treated with gliclazide). \"Similarly, for the rs4664443 GG genotype, the median HbA1c improvement in the study group was 0.69 (IQR, 0.48–0.91) compared with 1.25 (IQR, 1.00–1.46) in the control group (*P* < .001), indicating lower efficacy of sitagliptin.\"'),\n", + " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG genotype (better response) with sitagliptin treatment in the study group compared to the control group (treated with gliclazide). \"Conversely, patients with the rs3765467 AG genotype in the study group demonstrated a median HbA1c improvement of 1.42 (IQR, 1.22–1.68) compared with 1.08 (IQR, 0.97–1.15) in the control group (*P* = .023), indicating favorable responses to both treatments.\"'),\n", + " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC genotype (better response) with sitagliptin treatment in the study group compared to the control group (treated with gliclazide). \"For KCNJ11 gene polymorphisms, patients with the rs2285676 CC genotype in the study group had a median HbA1c improvement of 1.02 (IQR, 0.90–1.22), while the control group showed 1.31 (IQR, 1.08–1.42; *P* < .001), indicating more substantial insulin secretion capability with sitagliptin.\"'),\n", + " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT genotype (slower metabolism of gliclazide) in the study group compared to the control group. \"Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; *P* < .001).\"'),\n", + " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG genotype (slower metabolism of gliclazide) in the study group compared to the control group. \"For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (*P* = .464).\"')]" ] }, - "execution_count": 32, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -118,6 +122,44 @@ "source": [ "variants_debug = extract_all_variants(pmcid=\"PMC11730665\", debug=True)" ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'variant_id': 'rs2909451', 'gene': 'DPP4', 'allele': 'TT'}, {'variant_id': 'rs2285676', 'gene': 'KCNJ11', 'allele': 'CC'}, {'variant_id': 'rs163184', 'gene': 'KCNQ1', 'allele': 'GG'}, {'variant_id': 'rs7754840', 'gene': 'CDKAL1', 'allele': 'CG'}, {'variant_id': 'rs4664443', 'gene': 'DPP4', 'allele': 'GG'}, {'variant_id': 'rs1799853', 'gene': 'CYP2C9', 'allele': 'TT'}, {'variant_id': 'rs3765467', 'gene': 'GLP1R', 'allele': 'AG'}, {'variant_id': 'rs6923761', 'gene': 'GLP1R', 'allele': 'AA'}]\n" + ] + } + ], + "source": [ + "from src.utils import get_true_variants\n", + "\n", + "true_variants = get_true_variants(\"PMC11730665\")\n", + "print(true_variants)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from loguru import logger\n", + "from src.inference import Variant" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/test.ipynb b/notebooks/intermediate.ipynb similarity index 99% rename from notebooks/test.ipynb rename to notebooks/intermediate.ipynb index 4f4e795..e990a0e 100644 --- a/notebooks/test.ipynb +++ b/notebooks/intermediate.ipynb @@ -52,9 +52,9 @@ "metadata": {}, "outputs": [], "source": [ - "from src.annotation_extraction.inference import SimpleLLM\n", - "from src.annotation_extraction.prompts import PromptGenerator\n", - "from src.annotation_extraction.article_parser import MarkdownParser\n", + "from src.inference import Generator\n", + "from src.prompts import PromptGenerator\n", + "from src.article_parser import MarkdownParser\n", "from typing import List" ] }, @@ -138,11 +138,11 @@ "source": [ "from dotenv import load_dotenv\n", "load_dotenv()\n", - "from src.annotation_extraction.inference import VariantList, Variant\n", + "from src.inference import VariantList, Variant\n", "import json\n", "from loguru import logger\n", - "from src.annotation_extraction.utils import save_output\n", - "from src.annotation_extraction.components import extract_variants_list\n" + "from src.utils import save_output\n", + "from src.components.all_variants import extract_all_variants\n" ] }, { @@ -160,7 +160,7 @@ } ], "source": [ - "variant_list = extract_variants_list(pmcid=\"PMC11730665\")" + "variant_list = extract_all_variants(pmcid=\"PMC11730665\")" ] }, { @@ -5906,7 +5906,7 @@ "outputs": [], "source": [ "from tqdm import tqdm\n", - "from src.annotation_extraction.utils import compare_lists" + "from src.utils import compare_lists" ] }, { diff --git a/src/utils.py b/src/utils.py index d7cfaa5..840b769 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,7 +1,9 @@ import re -from termcolor import colored from loguru import logger - +import json +from typing import List +from termcolor import colored +from src.inference import Variant def extractVariantsRegex(text): # Note, seems to extract a ton of variants, not just the ones that are being studied @@ -18,3 +20,60 @@ def save_output(prompt, output, filename): f.write("\nOutput:\n") f.write(output) logger.info(f"Saved output to {filename}.txt") + +def compare_lists(experimental_list: List[str], ground_truth_list: List[str], pmcid: str): + """ + Compare experimental list with ground truth list and calculate performance metrics. + + Args: + experimental_list (list): List of predicted/experimental values + ground_truth_list (list): List of actual/ground truth values + pmcid (str): PMCID of the article + + Returns: + tuple: (true_positives, true_negatives, false_positives, false_negatives) + """ + # Convert lists to sets for efficient comparison + experimental_set = set(experimental_list) + ground_truth_set = set(ground_truth_list) + + # Calculate performance metrics + true_positives = len(experimental_set.intersection(ground_truth_set)) + false_positives = len(experimental_set - ground_truth_set) + false_negatives = len(ground_truth_set - experimental_set) + true_negatives = 0 # Not applicable in this context + + # Color-code the lists + colored_experimental = [] + colored_ground_truth = [] + + # Color experimental list + for item in experimental_list: + if item in ground_truth_set: + colored_experimental.append(colored(item, 'green')) + else: + colored_experimental.append(colored(item, 'red')) + + # Color ground truth list + for item in ground_truth_list: + if item in experimental_set: + colored_ground_truth.append(colored(item, 'green')) + else: + colored_ground_truth.append(colored(item, 'red')) + + # Print colored lists + print(f"================= {pmcid} =================") + print("Experimental List:") + print(' '.join(map(str, colored_experimental))) + print("\nGround Truth List:") + print(' '.join(map(str, colored_ground_truth))) + + # Return performance metrics + return true_positives, true_negatives, false_positives, false_negatives + +def get_true_variants(pmcid): + """ + Get the actual annotated variants for a given PMCID. + """ + true_variant_list = json.load(open("data/benchmark/true_variant_list.json")) + return true_variant_list[pmcid] \ No newline at end of file diff --git a/tests/utils.py b/tests/utils.py deleted file mode 100644 index f8cd412..0000000 --- a/tests/utils.py +++ /dev/null @@ -1,52 +0,0 @@ -from termcolor import colored -from typing import List - -def compare_lists(experimental_list: List[str], ground_truth_list: List[str], pmcid: str): - """ - Compare experimental list with ground truth list and calculate performance metrics. - - Args: - experimental_list (list): List of predicted/experimental values - ground_truth_list (list): List of actual/ground truth values - pmcid (str): PMCID of the article - - Returns: - tuple: (true_positives, true_negatives, false_positives, false_negatives) - """ - # Convert lists to sets for efficient comparison - experimental_set = set(experimental_list) - ground_truth_set = set(ground_truth_list) - - # Calculate performance metrics - true_positives = len(experimental_set.intersection(ground_truth_set)) - false_positives = len(experimental_set - ground_truth_set) - false_negatives = len(ground_truth_set - experimental_set) - true_negatives = 0 # Not applicable in this context - - # Color-code the lists - colored_experimental = [] - colored_ground_truth = [] - - # Color experimental list - for item in experimental_list: - if item in ground_truth_set: - colored_experimental.append(colored(item, 'green')) - else: - colored_experimental.append(colored(item, 'red')) - - # Color ground truth list - for item in ground_truth_list: - if item in experimental_set: - colored_ground_truth.append(colored(item, 'green')) - else: - colored_ground_truth.append(colored(item, 'red')) - - # Print colored lists - print(f"================= {pmcid} =================") - print("Experimental List:") - print(' '.join(map(str, colored_experimental))) - print("\nGround Truth List:") - print(' '.join(map(str, colored_ground_truth))) - - # Return performance metrics - return true_positives, true_negatives, false_positives, false_negatives \ No newline at end of file diff --git a/tests/variant_list_tests.py b/tests/variant_list_tests.py index 8995afe..763b537 100644 --- a/tests/variant_list_tests.py +++ b/tests/variant_list_tests.py @@ -1,8 +1,9 @@ from loguru import logger -from src.annotation_extraction.components import extract_variants_list +from src.components.all_variants import extract_all_variants import json from typing import List -from tests.utils import compare_lists +from src.utils import compare_lists +from typing import List def load_ground_truth(pmcid: str): try: @@ -51,7 +52,7 @@ def test_extract_function(pmcids: List[str] | str, verbose: bool = False): for pmcid in pmcids: logger.info(f"Testing PMCID: {pmcid}") ground_truth = parse_variant_list(load_ground_truth(pmcid)) - extracted = parse_variant_list(extract_variants_list(pmcid)) + extracted = parse_variant_list(extract_all_variants(pmcid)) contingencies = calc_contingencies(ground_truth, extracted) # update running contingencies running_contingencies["true_positives"] += contingencies["true_positives"] From c214a1bc4213055f124a842dcdd5046e42684d23 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 23:32:28 -0700 Subject: [PATCH 21/28] feat: better hydration, started associations --- docs/annotation_prompts.md | 44 +++++++++++++++ notebooks/all_variants.ipynb | 38 +++++++------ notebooks/intermediate.ipynb | 2 +- src/README.md | 2 +- src/components/all_variants.py | 6 +-- src/components/association_types.py | 84 +++++++++++++++++++++++++++++ src/inference.py | 83 ++++++++++++++++++++++++++-- src/prompts.py | 36 ++++++++++++- 8 files changed, 264 insertions(+), 31 deletions(-) create mode 100644 src/components/association_types.py diff --git a/docs/annotation_prompts.md b/docs/annotation_prompts.md index d19de43..0d3dd60 100644 --- a/docs/annotation_prompts.md +++ b/docs/annotation_prompts.md @@ -662,4 +662,48 @@ For each term, the output should be of the format: Extracted Output: (output) Reason: (one sentence justification) Quote: (sentence from the article that demonstrates why) +``` + +### Extract Association for Single Variant +``` +Article: \n\n{article_text}\n\n + +For the variant {variant_id}, determine what type of association(s) is being studied by the article. The options are Drug, Phenotype, and Functional. + +A variant has a Drug association when the article reports associations between the genetic variant and +pharmacological parameters or clinical drug response measures that specifically relate to: +- Pharmacokinetic/Pharmacodynamic Parameters +- Clinical phenotypes/adverse events (Drug toxicity, organ dysfunction, treatment response phenotypes, disease outcomes when treated with drugs) + +A variant has a Phenotype association when the article reports associations between genetic variants and adverse drug reactions, toxicities, or clinical outcomes that represent: +- Toxicity/Safety outcomes +- Clinical phenotypes/adverse events + +A variant has a Functional association when the article contains in vitro or mechanistic functional studies that directly measure how the variant affects: +- Enzyme/transporter activity (e.g., clearance, metabolism, transport) +- Binding affinity (e.g., protein-drug interactions) +- Functional properties (e.g., uptake rates, kinetic parameters like Km/Vmax) + +The key distinction is mechanistic functional studies typically get Functional associations vs clinical association studies get Phenotype and Drug associations but Functional. +Examples: +- "Cardiotoxicity when treated with anthracyclines" → Phenotype +- "Decreased clearance of methotrexate" → Drug +- "Decreased enzyme activity in cell culture" → Functional +- "Variant affects drug clearance/response" —> Drug +- "Variant affects adverse events/toxicity outcomes" —> Phenotype +- "Variant affects protein function in laboratory studies" —> Functional + +Using this information, decide which out of the 3 annotations the variant should receive with a one sentence summary explanation for the decision along with a sentence/quote from the article that indicates why this is true. It is possible there is more than one Annotation/association per variant + +Output Format: +Variant Drug Association: (Y/N) +Explanation: (Reason) +Quote:(Quote) + +Variant Phenotype Association: (Y/N) +Explanation: (Reason) +Quote:(Quote) + +Variant Functional Association: (Y/N) +Explanation: (Reason) ``` \ No newline at end of file diff --git a/notebooks/all_variants.ipynb b/notebooks/all_variants.ipynb index 641e769..82652cc 100644 --- a/notebooks/all_variants.ipynb +++ b/notebooks/all_variants.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 38, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# Run this cell: \n", "# The lines below will instruct jupyter to reload imported modules before \n", @@ -27,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -48,23 +39,30 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 22:06:23.108\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 22:06:23.110\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 22:06:23.111\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n" + "\u001b[32m2025-06-26 23:30:41.543\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:30:41.544\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:30:41.545\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m60\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-26 22:06:35.703\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mFound 6 variants\u001b[0m\n" + "ename": "AttributeError", + "evalue": "'HydratedPrompt' object has no attribute 'temperature'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcomponents\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mall_variants\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m extract_all_variants\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m variants = \u001b[43mextract_all_variants\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPMC11730665\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/components/all_variants.py:61\u001b[39m, in \u001b[36mextract_all_variants\u001b[39m\u001b[34m(article_text, pmcid, model, temperature, debug)\u001b[39m\n\u001b[32m 59\u001b[39m hydrated_prompt = prompt_generator.hydrate_prompt()\n\u001b[32m 60\u001b[39m logger.info(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExtracting all variants\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m61\u001b[39m output = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprompted_generate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhydrated_prompt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 62\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m debug:\n\u001b[32m 63\u001b[39m logger.debug(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRaw LLM output: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/inference.py:22\u001b[39m, in \u001b[36mLLMInterface.prompted_generate\u001b[39m\u001b[34m(self, HydratedPrompt)\u001b[39m\n\u001b[32m 21\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mprompted_generate\u001b[39m(\u001b[38;5;28mself\u001b[39m, HydratedPrompt: HydratedPrompt) -> \u001b[38;5;28mstr\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.generate(HydratedPrompt.input_prompt, HydratedPrompt.system_prompt, \u001b[43mHydratedPrompt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtemperature\u001b[49m, HydratedPrompt.response_format)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/pydantic/main.py:994\u001b[39m, in \u001b[36mBaseModel.__getattr__\u001b[39m\u001b[34m(self, item)\u001b[39m\n\u001b[32m 991\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__getattribute__\u001b[39m(item) \u001b[38;5;66;03m# Raises AttributeError if appropriate\u001b[39;00m\n\u001b[32m 992\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 993\u001b[39m \u001b[38;5;66;03m# this is the current error\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m994\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mitem\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m'\u001b[39m)\n", + "\u001b[31mAttributeError\u001b[39m: 'HydratedPrompt' object has no attribute 'temperature'" ] } ], diff --git a/notebooks/intermediate.ipynb b/notebooks/intermediate.ipynb index e990a0e..a3010ab 100644 --- a/notebooks/intermediate.ipynb +++ b/notebooks/intermediate.ipynb @@ -53,7 +53,7 @@ "outputs": [], "source": [ "from src.inference import Generator\n", - "from src.prompts import PromptGenerator\n", + "from src.prompts import GeneratorPrompt\n", "from src.article_parser import MarkdownParser\n", "from typing import List" ] diff --git a/src/README.md b/src/README.md index e947196..1140d2a 100644 --- a/src/README.md +++ b/src/README.md @@ -5,7 +5,7 @@ ``` model = Generator() article_text = MarkdownParser(pmcid="PMC11730665").get_article_text() -prompt_generator = PromptGenerator(, {"article_text": article_text}) +prompt_generator = GeneratorPrompt(, {"article_text": article_text}) prompt = prompt_generator.get_prompt() output = model.generate(prompt, response_format=) ``` \ No newline at end of file diff --git a/src/components/all_variants.py b/src/components/all_variants.py index 83ba09a..876bef0 100644 --- a/src/components/all_variants.py +++ b/src/components/all_variants.py @@ -1,5 +1,5 @@ from src.inference import Variant, VariantList, Generator -from src.prompts import PromptGenerator, PromptVariables +from src.prompts import GeneratorPrompt, PromptVariables from src.article_parser import MarkdownParser from loguru import logger import json @@ -55,10 +55,10 @@ def extract_all_variants( output_queues=VARIANT_LIST_OUTPUT_QUEUES, output_format_structure=VariantList ) - prompt_generator = PromptGenerator(prompt_variables) + prompt_generator = GeneratorPrompt(prompt_variables) hydrated_prompt = prompt_generator.hydrate_prompt() logger.info(f"Extracting all variants") - output = model.generate(hydrated_prompt.input_prompt, response_format=VariantList) + output = model.prompted_generate(hydrated_prompt) if debug: logger.debug(f"Raw LLM output: {output}") parsed_output = json.loads(output) diff --git a/src/components/association_types.py b/src/components/association_types.py new file mode 100644 index 0000000..05a1d60 --- /dev/null +++ b/src/components/association_types.py @@ -0,0 +1,84 @@ +""" +Given a list of variants and the article text, determine the type of association (drug, phenotype, functional association) +""" + +from src.components.all_variants import Variant +from typing import List +from src.prompts import PromptVariables, GeneratorPrompt, ParserPrompt +from src.inference import Generator, Parser +from pydantic import BaseModel + +class AssociationType(BaseModel): + """ + Association type. + """ + variant: Variant + drug_association: bool + drug_association_explanation: str + drug_association_quote: str + phenotype_association: bool + phenotype_association_explanation: str + phenotype_association_quote: str + functional_association: bool + functional_association_explanation: str + functional_association_quote: str + + +KEY_QUESTION = """ +For the variant {variant_id}, determine what type of association(s) is being studied by the article. The options are Drug, Phenotype, and Functional. + +A variant has a Drug association when the article reports associations between the genetic variant and +pharmacological parameters or clinical drug response measures that specifically relate to: +- Pharmacokinetic/Pharmacodynamic Parameters +- Clinical phenotypes/adverse events (Drug toxicity, organ dysfunction, treatment response phenotypes, disease outcomes when treated with drugs) + +A variant has a Phenotype association when the article reports associations between genetic variants and adverse drug reactions, toxicities, or clinical outcomes that represent: +- Toxicity/Safety outcomes +- Clinical phenotypes/adverse events + +A variant has a Functional association when the article contains in vitro or mechanistic functional studies that directly measure how the variant affects: +- Enzyme/transporter activity (e.g., clearance, metabolism, transport) +- Binding affinity (e.g., protein-drug interactions) +- Functional properties (e.g., uptake rates, kinetic parameters like Km/Vmax) + +The key distinction is mechanistic functional studies typically get Functional associations vs clinical association studies get Phenotype and Drug associations but Functional. +Examples: +- "Cardiotoxicity when treated with anthracyclines" → Phenotype +- "Decreased clearance of methotrexate" → Drug +- "Decreased enzyme activity in cell culture" → Functional +- "Variant affects drug clearance/response" —> Drug +- "Variant affects adverse events/toxicity outcomes" —> Phenotype +- "Variant affects protein function in laboratory studies" —> Functional + +""" + +OUTPUT_QUEUES = """ +Using this information, decide which out of the 3 annotations the variant should receive with a one sentence summary explanation for the decision along with a sentence/quote from the article that indicates why this is true. It is possible there is more than one Annotation/association per variant + +Variant Drug Association: (Y/N) +Explanation: (Reason) +Quote:(Quote) + +Variant Phenotype Association: (Y/N) +Explanation: (Reason) +Quote:(Quote) + +Variant Functional Association: (Y/N) +Explanation: (Reason) +""" + +def determine_association_type(variant: Variant, article_text: str) -> AssociationType: + prompt_variables = PromptVariables( + article_text=article_text, + key_question=KEY_QUESTION, + output_queues=OUTPUT_QUEUES, + output_format_structure=AssociationType, + ) + prompt_generator = GeneratorPrompt(prompt_variables) + generator_prompt = prompt_generator.hydrate_prompt() + generator = Generator(model="gpt-4o-mini", temperature=0.1) + response = generator.prompted_generate(generator_prompt) + parser = Parser(model="gpt-4o-mini", temperature=0.1) + parser_prompt = ParserPrompt(input_prompt=response, output_format_structure=AssociationType, system_prompt=generator_prompt.system_prompt) + parsed_response = parser.prompted_generate(parser_prompt) + return parsed_response diff --git a/src/inference.py b/src/inference.py index 69469a5..e049009 100644 --- a/src/inference.py +++ b/src/inference.py @@ -6,23 +6,53 @@ from dotenv import load_dotenv from pydantic import BaseModel, field_validator from typing import List +from src.prompts import HydratedPrompt load_dotenv() class LLMInterface: - """LLM interface.""" + """LLM Interface implemented by Generator and Parser classes""" def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): self.model = model self.temperature = temperature - def generate(self, prompt: str, temperature: float = None) -> str: - raise NotImplementedError("Subclasses must implement this method.") + def prompted_generate(self, HydratedPrompt: HydratedPrompt, temperature: float = None) -> str: + temp = temperature if temperature is not None else self.temperature + return self.generate(HydratedPrompt.input_prompt, HydratedPrompt.system_prompt, temp, HydratedPrompt.output_format_structure) + + def generate( + self, + prompt: str, + system_prompt: str = None, + temperature: float = None, + response_format: BaseModel = None, + ) -> str: + temp = temperature if temperature is not None else self.temperature + # Check if system prompt is provided + if system_prompt is not None and system_prompt != "": + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt}, + ] + else: + messages = [{"role": "user", "content": prompt}] + try: + response = litellm.completion( + model=self.model, + messages=messages, + response_format=response_format, + temperature=temp, + ) + except Exception as e: + logger.error(f"Error generating response: {e}") + raise e + return response.choices[0].message.content class Generator(LLMInterface): - """Simple LLM interface that just returns as response to the prompt.""" + """Generator Class""" debug_mode = False @@ -61,6 +91,51 @@ def generate( raise e return response.choices[0].message.content +class Parser(LLMInterface): + """Parser Class""" + + debug_mode = False + + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): + self.model = model + self.temperature = temperature + + if self.debug_mode: + litellm.set_verbose = True + + def generate( + self, + prompt: str, + response_format: BaseModel, + system_prompt: str = None, + temperature: float = None, + ) -> str: + temp = temperature if temperature is not None else self.temperature + # Check if system prompt is provided + if system_prompt is not None and system_prompt != "": + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt}, + ] + else: + logger.warning("No system prompt provided. Using default system prompt. System prompts recommended for parsing.") + messages = [ + {"role": "system", "content": "Your job is to parse the response into a structured output. Please provide your response in the exact format specified by the response_format parameter."}, + {"role": "user", "content": prompt}, + ] + try: + response = litellm.completion( + model=self.model, + messages=messages, + response_format=response_format, + temperature=temp, + ) + except Exception as e: + logger.error(f"Error generating response: {e}") + raise e + return response.choices[0].message.content + + class Variant(BaseModel): """Variant.""" diff --git a/src/prompts.py b/src/prompts.py index 4056822..0eba3ef 100644 --- a/src/prompts.py +++ b/src/prompts.py @@ -24,7 +24,15 @@ class PromptVariables(BaseModel): - """Input variables for prompt generation.""" + """Input variables for prompt generation. + + Members: + article_text: The text of the article. + key_question: The key question to answer. + output_queues: The output queues to use. + system_prompt: The system prompt to use. + output_format_structure: The output format structure to use. + """ article_text: str key_question: str @@ -41,7 +49,7 @@ class HydratedPrompt(BaseModel): output_format_structure: Optional[Type[BaseModel]] = None -class PromptGenerator: +class GeneratorPrompt: def __init__(self, prompt_variables: PromptVariables): self.prompt_template = PROMPT_TEMPLATE self.prompt_variables = prompt_variables @@ -53,3 +61,27 @@ def hydrate_prompt(self) -> HydratedPrompt: input_prompt=self.prompt_template.format(**self.prompt_variables.model_dump()), output_format_structure=self.prompt_variables.output_format_structure, ) + +class ParserPrompt(): + """Parser prompt generator.""" + + def __init__(self, input_prompt: str, output_format_structure: Type[BaseModel], system_prompt: str = None): + self.input_prompt = input_prompt + self.output_format_structure = output_format_structure + self.system_prompt = system_prompt + if self.system_prompt is None or self.system_prompt == "": + self.system_prompt = "Your job is to parse the response into a structured output. Please provide your response in the exact format specified by the output_format_structure parameter." + if self.input_prompt is None or self.input_prompt == "": + logger.error("Input prompt is required for parser prompt.") + raise ValueError("Input prompt is required for parser prompt.") + if self.output_format_structure is None: + logger.error("Output format structure is required for parser prompt.") + raise ValueError("Output format structure is required for parser prompt.") + + def hydrate_prompt(self) -> HydratedPrompt: + """Hydrate the prompt.""" + return HydratedPrompt( + system_prompt=self.system_prompt, + input_prompt=self.input_prompt, + output_format_structure=self.output_format_structure, + ) \ No newline at end of file From f521b213c57824feef0987e51ac1233588a4c8ea Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 23:32:58 -0700 Subject: [PATCH 22/28] chore: notebook updates --- notebooks/all_variants.ipynb | 105 ++++++----------------------------- notebooks/intermediate.ipynb | 17 ++---- 2 files changed, 21 insertions(+), 101 deletions(-) diff --git a/notebooks/all_variants.ipynb b/notebooks/all_variants.ipynb index 82652cc..a94597a 100644 --- a/notebooks/all_variants.ipynb +++ b/notebooks/all_variants.ipynb @@ -39,30 +39,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 23:30:41.543\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:30:41.544\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:30:41.545\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m60\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'HydratedPrompt' object has no attribute 'temperature'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcomponents\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mall_variants\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m extract_all_variants\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m variants = \u001b[43mextract_all_variants\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPMC11730665\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/components/all_variants.py:61\u001b[39m, in \u001b[36mextract_all_variants\u001b[39m\u001b[34m(article_text, pmcid, model, temperature, debug)\u001b[39m\n\u001b[32m 59\u001b[39m hydrated_prompt = prompt_generator.hydrate_prompt()\n\u001b[32m 60\u001b[39m logger.info(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExtracting all variants\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m61\u001b[39m output = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprompted_generate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhydrated_prompt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 62\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m debug:\n\u001b[32m 63\u001b[39m logger.debug(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRaw LLM output: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/inference.py:22\u001b[39m, in \u001b[36mLLMInterface.prompted_generate\u001b[39m\u001b[34m(self, HydratedPrompt)\u001b[39m\n\u001b[32m 21\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mprompted_generate\u001b[39m(\u001b[38;5;28mself\u001b[39m, HydratedPrompt: HydratedPrompt) -> \u001b[38;5;28mstr\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.generate(HydratedPrompt.input_prompt, HydratedPrompt.system_prompt, \u001b[43mHydratedPrompt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtemperature\u001b[49m, HydratedPrompt.response_format)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/pydantic/main.py:994\u001b[39m, in \u001b[36mBaseModel.__getattr__\u001b[39m\u001b[34m(self, item)\u001b[39m\n\u001b[32m 991\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__getattribute__\u001b[39m(item) \u001b[38;5;66;03m# Raises AttributeError if appropriate\u001b[39;00m\n\u001b[32m 992\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 993\u001b[39m \u001b[38;5;66;03m# this is the current error\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m994\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mitem\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m'\u001b[39m)\n", - "\u001b[31mAttributeError\u001b[39m: 'HydratedPrompt' object has no attribute 'temperature'" + "\u001b[32m2025-06-26 23:31:44.522\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:31:44.524\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:31:44.524\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m60\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n", + "\u001b[32m2025-06-26 23:31:58.678\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mFound 10 variants\u001b[0m\n" ] } ], @@ -74,21 +61,25 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[Variant(variant_id='rs2909451', gene='DPP4', allele='TT genotype (lower efficacy) with sitagliptin treatment in the study group compared to the control group (treated with gliclazide). \"Patients with the rs2909451 TT genotype in the study group (treated with sitagliptin) exhibited a median HbA1c improvement of 0.57 (interquartile range [IQR], 0.18–0.85), whereas the control group (treated with gliclazide) showed a median improvement of 1.11 (IQR, 0.86–1.35; *P* < .001).\"'),\n", - " Variant(variant_id='rs4664443', gene='DPP4', allele='GG genotype (lower efficacy) with sitagliptin treatment in the study group compared to the control group (treated with gliclazide). \"Similarly, for the rs4664443 GG genotype, the median HbA1c improvement in the study group was 0.69 (IQR, 0.48–0.91) compared with 1.25 (IQR, 1.00–1.46) in the control group (*P* < .001), indicating lower efficacy of sitagliptin.\"'),\n", - " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG genotype (better response) with sitagliptin treatment in the study group compared to the control group (treated with gliclazide). \"Conversely, patients with the rs3765467 AG genotype in the study group demonstrated a median HbA1c improvement of 1.42 (IQR, 1.22–1.68) compared with 1.08 (IQR, 0.97–1.15) in the control group (*P* = .023), indicating favorable responses to both treatments.\"'),\n", - " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC genotype (better response) with sitagliptin treatment in the study group compared to the control group (treated with gliclazide). \"For KCNJ11 gene polymorphisms, patients with the rs2285676 CC genotype in the study group had a median HbA1c improvement of 1.02 (IQR, 0.90–1.22), while the control group showed 1.31 (IQR, 1.08–1.42; *P* < .001), indicating more substantial insulin secretion capability with sitagliptin.\"'),\n", - " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT genotype (slower metabolism of gliclazide) in the study group compared to the control group. \"Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; *P* < .001).\"'),\n", - " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG genotype (slower metabolism of gliclazide) in the study group compared to the control group. \"For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (*P* = .464).\"')]" + "[Variant(variant_id='rs2909451', gene='DPP4', allele='TT', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", + " Variant(variant_id='rs4664443', gene='DPP4', allele='GG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", + " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", + " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", + " Variant(variant_id='rs6923761', gene='GLP1R', allele='AA', evidence='Regarding GLP1R gene polymorphisms, patients with the rs6923761 AA homozygous genotype in the study group had a median HbA1c improvement of 0.90 (IQR, 0.61–1.01), while the control group showed 1.41 (IQR, 1.12–1.45; P = .010), suggesting reduced glycemic response to sitagliptin.'),\n", + " Variant(variant_id='rs163184', gene='KCNQ1', allele='GG', evidence='KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group (P < .001), suggesting lower responsiveness to sitagliptin and better response to gliclazide.'),\n", + " Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs7754840 CG genotype showed a median improvement of 1.44 (IQR, 1.38–1.72) in the study group compared with 1.09 (IQR, 0.79–1.17) in the control group (P = .053).'),\n", + " Variant(variant_id='rs756992', gene='CDKAL1', allele='AG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs756992 AG genotype exhibited a median improvement of 1.43 (IQR, 1.28–1.52) in the study group compared with 1.10 (IQR, 0.87–1.18) in the control group (P = .081).'),\n", + " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT', evidence='CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).'),\n", + " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (P = .464).')]" ] }, - "execution_count": 44, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -96,68 +87,6 @@ "source": [ "variants" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Test to see if variants are now Variant objects\n", - "print(f\"Type of first variant: {type(variants[0])}\")\n", - "print(f\"First variant: {variants[0]}\")\n", - "if hasattr(variants[0], 'evidence'):\n", - " print(f\"Evidence field: {variants[0].evidence}\")\n", - "else:\n", - " print(\"No evidence attribute found\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "variants_debug = extract_all_variants(pmcid=\"PMC11730665\", debug=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'variant_id': 'rs2909451', 'gene': 'DPP4', 'allele': 'TT'}, {'variant_id': 'rs2285676', 'gene': 'KCNJ11', 'allele': 'CC'}, {'variant_id': 'rs163184', 'gene': 'KCNQ1', 'allele': 'GG'}, {'variant_id': 'rs7754840', 'gene': 'CDKAL1', 'allele': 'CG'}, {'variant_id': 'rs4664443', 'gene': 'DPP4', 'allele': 'GG'}, {'variant_id': 'rs1799853', 'gene': 'CYP2C9', 'allele': 'TT'}, {'variant_id': 'rs3765467', 'gene': 'GLP1R', 'allele': 'AG'}, {'variant_id': 'rs6923761', 'gene': 'GLP1R', 'allele': 'AA'}]\n" - ] - } - ], - "source": [ - "from src.utils import get_true_variants\n", - "\n", - "true_variants = get_true_variants(\"PMC11730665\")\n", - "print(true_variants)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from loguru import logger\n", - "from src.inference import Variant" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/notebooks/intermediate.ipynb b/notebooks/intermediate.ipynb index a3010ab..53b814e 100644 --- a/notebooks/intermediate.ipynb +++ b/notebooks/intermediate.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# Run this cell: \n", "# The lines below will instruct jupyter to reload imported modules before \n", @@ -27,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -48,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ From 167a61a76ff3296e9553e45ea85d366fa09ff03e Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Thu, 26 Jun 2025 23:53:20 -0700 Subject: [PATCH 23/28] feat: list all the associations --- ...ariants.ipynb => testing_components.ipynb} | 88 ++++++++++++- src/components/all_variants.py | 12 +- src/components/association_types.py | 53 +++++++- src/inference.py | 120 +++--------------- src/utils.py | 20 ++- 5 files changed, 169 insertions(+), 124 deletions(-) rename notebooks/{all_variants.ipynb => testing_components.ipynb} (65%) diff --git a/notebooks/all_variants.ipynb b/notebooks/testing_components.ipynb similarity index 65% rename from notebooks/all_variants.ipynb rename to notebooks/testing_components.ipynb index a94597a..fbcff6f 100644 --- a/notebooks/all_variants.ipynb +++ b/notebooks/testing_components.ipynb @@ -39,17 +39,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 23:31:44.522\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:31:44.524\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:31:44.524\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m60\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n", - "\u001b[32m2025-06-26 23:31:58.678\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mFound 10 variants\u001b[0m\n" + "\u001b[32m2025-06-26 23:42:47.357\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:42:47.358\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:42:47.359\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n", + "\u001b[32m2025-06-26 23:43:01.195\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m26\u001b[0m - \u001b[1mFound 10 variants\u001b[0m\n" ] } ], @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -79,7 +79,7 @@ " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (P = .464).')]" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -87,6 +87,80 @@ "source": [ "variants" ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "variant_id='rs2909451' gene='DPP4' allele='TT' evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'\n" + ] + } + ], + "source": [ + "test_variant = variants[0]\n", + "print(test_variant)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from src.components.association_types import determine_association_type, list_association_types" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:43:38.957\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:43:38.961\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" + ] + } + ], + "source": [ + "association = determine_association_type(test_variant, pmcid=\"PMC11730665\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'str' object has no attribute 'variant'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mlist_association_types\u001b[49m\u001b[43m(\u001b[49m\u001b[43massociation\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m:3\u001b[39m, in \u001b[36mlist_association_types\u001b[39m\u001b[34m(association_type, debug)\u001b[39m\n", + "\u001b[31mAttributeError\u001b[39m: 'str' object has no attribute 'variant'" + ] + } + ], + "source": [ + "list_association_types(association)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/components/all_variants.py b/src/components/all_variants.py index 876bef0..066a897 100644 --- a/src/components/all_variants.py +++ b/src/components/all_variants.py @@ -1,6 +1,6 @@ from src.inference import Variant, VariantList, Generator from src.prompts import GeneratorPrompt, PromptVariables -from src.article_parser import MarkdownParser +from src.utils import get_article_text from loguru import logger import json from typing import List @@ -34,15 +34,7 @@ def extract_all_variants( Returns: A list of variants. """ - if article_text is None and pmcid is None: - logger.error("Either article_text or pmcid must be provided.") - raise ValueError("Either article_text or pmcid must be provided.") - - if article_text is not None and pmcid is not None: - logger.warning("Both article_text and PMCID are provided. Using article_text.") - - if article_text is None: - article_text = MarkdownParser(pmcid=pmcid).get_article_text() + article_text = get_article_text(pmcid=pmcid, article_text=article_text) if debug: logger.debug(f"Model: {model}, Temperature: {temperature}") diff --git a/src/components/association_types.py b/src/components/association_types.py index 05a1d60..a4792a4 100644 --- a/src/components/association_types.py +++ b/src/components/association_types.py @@ -7,6 +7,8 @@ from src.prompts import PromptVariables, GeneratorPrompt, ParserPrompt from src.inference import Generator, Parser from pydantic import BaseModel +from src.utils import get_article_text +from loguru import logger class AssociationType(BaseModel): """ @@ -67,18 +69,61 @@ class AssociationType(BaseModel): Explanation: (Reason) """ -def determine_association_type(variant: Variant, article_text: str) -> AssociationType: +def determine_association_type(variant: Variant, article_text: str = None, pmcid: str = None) -> AssociationType: + article_text = get_article_text(pmcid=pmcid, article_text=article_text) prompt_variables = PromptVariables( article_text=article_text, - key_question=KEY_QUESTION, + key_question=KEY_QUESTION.format(variant_id=variant.variant_id), output_queues=OUTPUT_QUEUES, output_format_structure=AssociationType, ) + logger.info(f"Determining association type for variant {variant.variant_id}") prompt_generator = GeneratorPrompt(prompt_variables) generator_prompt = prompt_generator.hydrate_prompt() + + # Step 1: Generate the analysis generator = Generator(model="gpt-4o-mini", temperature=0.1) response = generator.prompted_generate(generator_prompt) + + # Step 2: Parse the response into structured format parser = Parser(model="gpt-4o-mini", temperature=0.1) - parser_prompt = ParserPrompt(input_prompt=response, output_format_structure=AssociationType, system_prompt=generator_prompt.system_prompt) + parser_prompt = ParserPrompt( + input_prompt=response, + output_format_structure=AssociationType, + system_prompt=generator_prompt.system_prompt + ) parsed_response = parser.prompted_generate(parser_prompt) - return parsed_response + + # Parse the string response into an AssociationType object + try: + import json + parsed_dict = json.loads(parsed_response) + # Add the variant to the parsed data + parsed_dict['variant'] = variant + return AssociationType(**parsed_dict) + except (json.JSONDecodeError, TypeError) as e: + logger.error(f"Failed to parse response for variant {variant.variant_id}: {e}") + return None + +def list_association_types(association_type: AssociationType, debug: bool = False) -> List[str]: + association_types = [] + logger.info(f"Variant: {association_type.variant.variant_id}") + if association_type.drug_association: + association_types.append("Drug") + if debug: + logger.debug(f"Drug Association: {association_type.drug_association}") + logger.debug(f"Drug Association Explanation: {association_type.drug_association_explanation}") + logger.debug(f"Drug Association Quote: {association_type.drug_association_quote}") + if association_type.phenotype_association: + association_types.append("Phenotype") + if debug: + logger.debug(f"Phenotype Association: {association_type.phenotype_association}") + logger.debug(f"Phenotype Association Explanation: {association_type.phenotype_association_explanation}") + logger.debug(f"Phenotype Association Quote: {association_type.phenotype_association_quote}") + if association_type.functional_association: + association_types.append("Functional") + if debug: + logger.debug(f"Functional Association: {association_type.functional_association}") + logger.debug(f"Functional Association Explanation: {association_type.functional_association_explanation}") + logger.debug(f"Functional Association Quote: {association_type.functional_association_quote}") + return association_types \ No newline at end of file diff --git a/src/inference.py b/src/inference.py index e049009..91fb0ff 100644 --- a/src/inference.py +++ b/src/inference.py @@ -1,27 +1,31 @@ -from typing import Dict from loguru import logger import litellm -import re -import os -from dotenv import load_dotenv -from pydantic import BaseModel, field_validator from typing import List +from dotenv import load_dotenv +from pydantic import BaseModel +from abc import ABC, abstractmethod from src.prompts import HydratedPrompt load_dotenv() -class LLMInterface: +class LLMInterface(ABC): """LLM Interface implemented by Generator and Parser classes""" def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): self.model = model self.temperature = temperature - def prompted_generate(self, HydratedPrompt: HydratedPrompt, temperature: float = None) -> str: + def prompted_generate(self, hydrated_prompt: HydratedPrompt, temperature: float = None) -> str: temp = temperature if temperature is not None else self.temperature - return self.generate(HydratedPrompt.input_prompt, HydratedPrompt.system_prompt, temp, HydratedPrompt.output_format_structure) + return self.generate( + hydrated_prompt.input_prompt, + hydrated_prompt.system_prompt, + temp, + hydrated_prompt.output_format_structure + ) + @abstractmethod def generate( self, prompt: str, @@ -29,26 +33,8 @@ def generate( temperature: float = None, response_format: BaseModel = None, ) -> str: - temp = temperature if temperature is not None else self.temperature - # Check if system prompt is provided - if system_prompt is not None and system_prompt != "": - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": prompt}, - ] - else: - messages = [{"role": "user", "content": prompt}] - try: - response = litellm.completion( - model=self.model, - messages=messages, - response_format=response_format, - temperature=temp, - ) - except Exception as e: - logger.error(f"Error generating response: {e}") - raise e - return response.choices[0].message.content + """Generate a response from the LLM.""" + pass class Generator(LLMInterface): @@ -57,9 +43,7 @@ class Generator(LLMInterface): debug_mode = False def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): - self.model = model - self.temperature = temperature - + super().__init__(model, temperature) if self.debug_mode: litellm.set_verbose = True @@ -91,24 +75,23 @@ def generate( raise e return response.choices[0].message.content + class Parser(LLMInterface): """Parser Class""" debug_mode = False def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): - self.model = model - self.temperature = temperature - + super().__init__(model, temperature) if self.debug_mode: litellm.set_verbose = True def generate( self, prompt: str, - response_format: BaseModel, system_prompt: str = None, temperature: float = None, + response_format: BaseModel = None, ) -> str: temp = temperature if temperature is not None else self.temperature # Check if system prompt is provided @@ -145,8 +128,6 @@ class Variant(BaseModel): allele: str | None = None evidence: str | None = None - # TODO: Add field validation for gene and allele - class VariantList(BaseModel): """List of variants.""" @@ -154,69 +135,4 @@ class VariantList(BaseModel): variant_list: List[Variant] -class StrEntry(BaseModel): - """String entry.""" - - text: str - reason: str - quote: str - - -class EnumEntry(BaseModel): - """Enum entry.""" - - options: List[str] - text: str - reason: str - quote: str - - @field_validator("text") - @classmethod - def validate_text_in_options(cls, v: str, info) -> str: - if "options" in info.data and v not in info.data["options"]: - raise ValueError(f"Text {v} not in options {info.data['options']}") - return v - - -class VarDrugResponse(BaseModel): - """Response from VarDrug.""" - - variant_id: StrEntry - drug_id: StrEntry - effect: StrEntry - evidence: StrEntry - - -class VarPhenoResponse(BaseModel): - """Response from VarPheno.""" - variant_id: StrEntry - phenotype: StrEntry - evidence: StrEntry - - -class VarFAResponse(BaseModel): - """Response from VarFA.""" - - variant_id: StrEntry - phenotype: StrEntry - evidence: StrEntry - - -class VarDrugParser(LLMInterface): - """LLM interface that parses a VarDrug response into a dictionary.""" - - def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): - super().__init__(model, temperature) - if self.debug_mode: - litellm.set_verbose = True - - def generate(self, prompt: str, temperature: float = None) -> str: - temp = temperature if temperature is not None else self.temperature - response = litellm.completion( - model=self.model, - messages=[{"role": "user", "content": prompt}], - structured_output=True, - temperature=temp, - ) - return response.choices[0].message.content diff --git a/src/utils.py b/src/utils.py index 840b769..41b841a 100644 --- a/src/utils.py +++ b/src/utils.py @@ -4,6 +4,7 @@ from typing import List from termcolor import colored from src.inference import Variant +from src.article_parser import MarkdownParser def extractVariantsRegex(text): # Note, seems to extract a ton of variants, not just the ones that are being studied @@ -76,4 +77,21 @@ def get_true_variants(pmcid): Get the actual annotated variants for a given PMCID. """ true_variant_list = json.load(open("data/benchmark/true_variant_list.json")) - return true_variant_list[pmcid] \ No newline at end of file + return true_variant_list[pmcid] + + +def get_article_text(pmcid: str = None, article_text: str = None): + """ + Get the article text for a given PMCID or return the article text if it is already provided. + """ + if article_text is None and pmcid is None: + logger.error("Either article_text or pmcid must be provided.") + raise ValueError("Either article_text or pmcid must be provided.") + + if article_text is not None and pmcid is not None: + logger.warning("Both article_text and PMCID are provided. Using article_text.") + + if article_text is None: + article_text = MarkdownParser(pmcid=pmcid).get_article_text() + + return article_text \ No newline at end of file From c073841571ce041feaddcb5ef722af261b268acb Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Fri, 27 Jun 2025 00:35:44 -0700 Subject: [PATCH 24/28] feat: variant association types --- notebooks/testing_components.ipynb | 197 +++++++++++++++++++++++++--- src/components/association_types.py | 45 +++++-- src/prompts.py | 10 +- 3 files changed, 216 insertions(+), 36 deletions(-) diff --git a/notebooks/testing_components.ipynb b/notebooks/testing_components.ipynb index fbcff6f..23103d3 100644 --- a/notebooks/testing_components.ipynb +++ b/notebooks/testing_components.ipynb @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -108,51 +108,212 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "from src.components.association_types import determine_association_type, list_association_types" + "from src.components.association_types import get_association_types, list_association_types" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 23:43:38.957\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:43:38.961\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" + "\u001b[32m2025-06-27 00:20:59.201\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-27 00:20:59.203\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-27 00:20:59.204\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mget_association_types\u001b[0m:\u001b[36m5\u001b[0m - \u001b[1mDetermining association type for variants [Variant(variant_id='rs2909451', gene='DPP4', allele='TT', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'), Variant(variant_id='rs4664443', gene='DPP4', allele='GG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'), Variant(variant_id='rs3765467', gene='GLP1R', allele='AG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'), Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'), Variant(variant_id='rs6923761', gene='GLP1R', allele='AA', evidence='Regarding GLP1R gene polymorphisms, patients with the rs6923761 AA homozygous genotype in the study group had a median HbA1c improvement of 0.90 (IQR, 0.61–1.01), while the control group showed 1.41 (IQR, 1.12–1.45; P = .010), suggesting reduced glycemic response to sitagliptin.'), Variant(variant_id='rs163184', gene='KCNQ1', allele='GG', evidence='KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group (P < .001), suggesting lower responsiveness to sitagliptin and better response to gliclazide.'), Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs7754840 CG genotype showed a median improvement of 1.44 (IQR, 1.38–1.72) in the study group compared with 1.09 (IQR, 0.79–1.17) in the control group (P = .053).'), Variant(variant_id='rs756992', gene='CDKAL1', allele='AG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs756992 AG genotype exhibited a median improvement of 1.43 (IQR, 1.28–1.52) in the study group compared with 1.10 (IQR, 0.87–1.18) in the control group (P = .081).'), Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT', evidence='CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).'), Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (P = .464).')]\u001b[0m\n" ] } ], "source": [ - "association = determine_association_type(test_variant, pmcid=\"PMC11730665\")" + "association = get_association_types(variants, pmcid=\"PMC11730665\")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'str' object has no attribute 'variant'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mlist_association_types\u001b[49m\u001b[43m(\u001b[49m\u001b[43massociation\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m:3\u001b[39m, in \u001b[36mlist_association_types\u001b[39m\u001b[34m(association_type, debug)\u001b[39m\n", - "\u001b[31mAttributeError\u001b[39m: 'str' object has no attribute 'variant'" + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:56:27.106\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:27.108\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:27.109\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs2909451\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:39.521\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs2909451 has association types: ['Drug', 'Phenotype']\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:39.523\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:39.526\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:39.529\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs4664443\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug', 'Phenotype']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:56:48.218\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs4664443 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:48.219\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:48.221\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:56:48.222\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs3765467\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:57:03.351\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs3765467 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:03.353\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:03.355\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:03.356\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs2285676\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:57:17.459\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs2285676 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:17.461\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:17.463\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:17.465\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs6923761\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:57:36.520\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs6923761 has association types: ['Drug', 'Phenotype']\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:36.524\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:36.527\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:36.530\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs163184\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug', 'Phenotype']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:57:53.632\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs163184 has association types: ['Drug', 'Phenotype']\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:53.634\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:53.636\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:57:53.637\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs7754840\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug', 'Phenotype']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:58:03.136\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs7754840 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:03.139\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:03.141\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:03.141\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs756992\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:58:11.293\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs756992 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:11.297\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:11.301\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:11.305\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs1799853\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:58:21.112\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs1799853 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:21.114\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:21.116\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-26 23:58:21.118\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs1057910\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-26 23:58:33.535\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs1057910 has association types: ['Drug', 'Functional']\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Drug', 'Functional']\n" ] } ], "source": [ - "list_association_types(association)" + "for variant in variants:\n", + " association = determine_association_type(variant, pmcid=\"PMC11730665\")\n", + " print(list_association_types(association))" ] }, { diff --git a/src/components/association_types.py b/src/components/association_types.py index a4792a4..cf0e678 100644 --- a/src/components/association_types.py +++ b/src/components/association_types.py @@ -10,6 +10,7 @@ from src.utils import get_article_text from loguru import logger + class AssociationType(BaseModel): """ Association type. @@ -25,9 +26,16 @@ class AssociationType(BaseModel): functional_association_explanation: str functional_association_quote: str +class AssociationTypeList(BaseModel): + """ + List of association types for structured output. + """ + association_types: List[AssociationType] + KEY_QUESTION = """ -For the variant {variant_id}, determine what type of association(s) is being studied by the article. The options are Drug, Phenotype, and Functional. +For the following variants, determine what type of association(s) is being studied by the article. The options are Drug, Phenotype, and Functional. +Variants: {variants} A variant has a Drug association when the article reports associations between the genetic variant and pharmacological parameters or clinical drug response measures that specifically relate to: @@ -57,6 +65,7 @@ class AssociationType(BaseModel): OUTPUT_QUEUES = """ Using this information, decide which out of the 3 annotations the variant should receive with a one sentence summary explanation for the decision along with a sentence/quote from the article that indicates why this is true. It is possible there is more than one Annotation/association per variant +Variant Object: (variant) Variant Drug Association: (Y/N) Explanation: (Reason) Quote:(Quote) @@ -69,15 +78,16 @@ class AssociationType(BaseModel): Explanation: (Reason) """ -def determine_association_type(variant: Variant, article_text: str = None, pmcid: str = None) -> AssociationType: +def get_association_types(variants: List[Variant], article_text: str = None, pmcid: str = None) -> List[AssociationType]: article_text = get_article_text(pmcid=pmcid, article_text=article_text) + variant_id_list = [variant.variant_id for variant in variants] prompt_variables = PromptVariables( article_text=article_text, - key_question=KEY_QUESTION.format(variant_id=variant.variant_id), + key_question=KEY_QUESTION.format(variants=variants), output_queues=OUTPUT_QUEUES, - output_format_structure=AssociationType, + output_format_structure=AssociationTypeList, ) - logger.info(f"Determining association type for variant {variant.variant_id}") + logger.info(f"Determining association type for variants {variant_id_list}") prompt_generator = GeneratorPrompt(prompt_variables) generator_prompt = prompt_generator.hydrate_prompt() @@ -89,25 +99,33 @@ def determine_association_type(variant: Variant, article_text: str = None, pmcid parser = Parser(model="gpt-4o-mini", temperature=0.1) parser_prompt = ParserPrompt( input_prompt=response, - output_format_structure=AssociationType, + output_format_structure=AssociationTypeList, system_prompt=generator_prompt.system_prompt ) parsed_response = parser.prompted_generate(parser_prompt) - # Parse the string response into an AssociationType object + # Parse the string response into AssociationType objects try: import json - parsed_dict = json.loads(parsed_response) - # Add the variant to the parsed data - parsed_dict['variant'] = variant - return AssociationType(**parsed_dict) + parsed_data = json.loads(parsed_response) + + # Handle different response formats + if isinstance(parsed_data, dict) and 'association_types' in parsed_data: + association_data = parsed_data['association_types'] + elif isinstance(parsed_data, list): + association_data = parsed_data + else: + association_data = [parsed_data] + + # Convert to AssociationType objects + return [AssociationType(**item) for item in association_data] + except (json.JSONDecodeError, TypeError) as e: - logger.error(f"Failed to parse response for variant {variant.variant_id}: {e}") + logger.error(f"Failed to parse response for variants {variants}: {e}") return None def list_association_types(association_type: AssociationType, debug: bool = False) -> List[str]: association_types = [] - logger.info(f"Variant: {association_type.variant.variant_id}") if association_type.drug_association: association_types.append("Drug") if debug: @@ -126,4 +144,5 @@ def list_association_types(association_type: AssociationType, debug: bool = Fals logger.debug(f"Functional Association: {association_type.functional_association}") logger.debug(f"Functional Association Explanation: {association_type.functional_association_explanation}") logger.debug(f"Functional Association Quote: {association_type.functional_association_quote}") + logger.info(f"Variant: {association_type.variant.variant_id} has association types: {association_types}") return association_types \ No newline at end of file diff --git a/src/prompts.py b/src/prompts.py index 0eba3ef..0c5253d 100644 --- a/src/prompts.py +++ b/src/prompts.py @@ -1,4 +1,4 @@ -from typing import Dict, Optional, Type +from typing import Optional, Type, List, Union from loguru import logger from pydantic import BaseModel @@ -12,7 +12,7 @@ - output format """ -PROMPT_TEMPLATE = """ +GENERATOR_PROMPT_TEMPLATE = """ You are an expert pharmacogenomics researcher reading and extracting key information from the following article: {article_text} @@ -38,7 +38,7 @@ class PromptVariables(BaseModel): key_question: str output_queues: Optional[str] = None system_prompt: Optional[str] = None - output_format_structure: Optional[Type[BaseModel]] = None + output_format_structure: Optional[Union[Type[BaseModel], List[Type[BaseModel]]]] = None class HydratedPrompt(BaseModel): @@ -51,7 +51,7 @@ class HydratedPrompt(BaseModel): class GeneratorPrompt: def __init__(self, prompt_variables: PromptVariables): - self.prompt_template = PROMPT_TEMPLATE + self.prompt_template = GENERATOR_PROMPT_TEMPLATE self.prompt_variables = prompt_variables def hydrate_prompt(self) -> HydratedPrompt: @@ -70,7 +70,7 @@ def __init__(self, input_prompt: str, output_format_structure: Type[BaseModel], self.output_format_structure = output_format_structure self.system_prompt = system_prompt if self.system_prompt is None or self.system_prompt == "": - self.system_prompt = "Your job is to parse the response into a structured output. Please provide your response in the exact format specified by the output_format_structure parameter." + self.system_prompt = "Your job is to parse the response into a structured output. Please provide your response in the exact format specified. If something is not clear, leave the value as None" if self.input_prompt is None or self.input_prompt == "": logger.error("Input prompt is required for parser prompt.") raise ValueError("Input prompt is required for parser prompt.") From b3dda938d04fb23cd54aaa0e7a7ad9e405f7d93e Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Fri, 27 Jun 2025 00:36:21 -0700 Subject: [PATCH 25/28] chore: black formatting --- src/components/all_variants.py | 25 +++++----- src/components/association_types.py | 74 ++++++++++++++++++++--------- src/inference.py | 25 +++++----- src/prompts.py | 20 ++++++-- src/utils.py | 35 ++++++++------ 5 files changed, 114 insertions(+), 65 deletions(-) diff --git a/src/components/all_variants.py b/src/components/all_variants.py index 066a897..02c3760 100644 --- a/src/components/all_variants.py +++ b/src/components/all_variants.py @@ -45,7 +45,7 @@ def extract_all_variants( article_text=article_text, key_question=VARIANT_LIST_KEY_QUESTION, output_queues=VARIANT_LIST_OUTPUT_QUEUES, - output_format_structure=VariantList + output_format_structure=VariantList, ) prompt_generator = GeneratorPrompt(prompt_variables) hydrated_prompt = prompt_generator.hydrate_prompt() @@ -56,20 +56,23 @@ def extract_all_variants( parsed_output = json.loads(output) if debug: logger.debug(f"Parsed output: {parsed_output}") - variant_list = [Variant(**variant_data) for variant_data in parsed_output["variant_list"]] + variant_list = [ + Variant(**variant_data) for variant_data in parsed_output["variant_list"] + ] logger.info(f"Found {len(variant_list)} variants") return variant_list -def main(pmcid: str, model: str = "gpt-4o", temperature: float = 0.1, output: str = None): + +def main( + pmcid: str, model: str = "gpt-4o", temperature: float = 0.1, output: str = None +): """Main function to demonstrate variant extraction functionality.""" try: # Extract variants variants = extract_all_variants( - pmcid=pmcid, - model=model, - temperature=temperature + pmcid=pmcid, model=model, temperature=temperature ) - + # Print results print(f"Found {len(variants)} variants:") for i, variant in enumerate(variants, 1): @@ -78,13 +81,13 @@ def main(pmcid: str, model: str = "gpt-4o", temperature: float = 0.1, output: st print(f" Allele: {variant.allele}") print(f" Evidence: {variant.evidence}") print() - + # Save to file if output path specified if output: - with open(output, 'w') as f: + with open(output, "w") as f: json.dump({"variants": variants}, f, indent=2) print(f"Results saved to {output}") - + except Exception as e: logger.error(f"Error extracting variants: {e}") - raise \ No newline at end of file + raise diff --git a/src/components/association_types.py b/src/components/association_types.py index cf0e678..ef793db 100644 --- a/src/components/association_types.py +++ b/src/components/association_types.py @@ -15,6 +15,7 @@ class AssociationType(BaseModel): """ Association type. """ + variant: Variant drug_association: bool drug_association_explanation: str @@ -26,10 +27,12 @@ class AssociationType(BaseModel): functional_association_explanation: str functional_association_quote: str + class AssociationTypeList(BaseModel): """ List of association types for structured output. """ + association_types: List[AssociationType] @@ -78,7 +81,10 @@ class AssociationTypeList(BaseModel): Explanation: (Reason) """ -def get_association_types(variants: List[Variant], article_text: str = None, pmcid: str = None) -> List[AssociationType]: + +def get_association_types( + variants: List[Variant], article_text: str = None, pmcid: str = None +) -> List[AssociationType]: article_text = get_article_text(pmcid=pmcid, article_text=article_text) variant_id_list = [variant.variant_id for variant in variants] prompt_variables = PromptVariables( @@ -90,59 +96,81 @@ def get_association_types(variants: List[Variant], article_text: str = None, pmc logger.info(f"Determining association type for variants {variant_id_list}") prompt_generator = GeneratorPrompt(prompt_variables) generator_prompt = prompt_generator.hydrate_prompt() - + # Step 1: Generate the analysis generator = Generator(model="gpt-4o-mini", temperature=0.1) response = generator.prompted_generate(generator_prompt) - + # Step 2: Parse the response into structured format parser = Parser(model="gpt-4o-mini", temperature=0.1) parser_prompt = ParserPrompt( - input_prompt=response, - output_format_structure=AssociationTypeList, - system_prompt=generator_prompt.system_prompt + input_prompt=response, + output_format_structure=AssociationTypeList, + system_prompt=generator_prompt.system_prompt, ) parsed_response = parser.prompted_generate(parser_prompt) - + # Parse the string response into AssociationType objects try: import json + parsed_data = json.loads(parsed_response) - + # Handle different response formats - if isinstance(parsed_data, dict) and 'association_types' in parsed_data: - association_data = parsed_data['association_types'] + if isinstance(parsed_data, dict) and "association_types" in parsed_data: + association_data = parsed_data["association_types"] elif isinstance(parsed_data, list): association_data = parsed_data else: association_data = [parsed_data] - + # Convert to AssociationType objects return [AssociationType(**item) for item in association_data] - + except (json.JSONDecodeError, TypeError) as e: logger.error(f"Failed to parse response for variants {variants}: {e}") return None -def list_association_types(association_type: AssociationType, debug: bool = False) -> List[str]: + +def list_association_types( + association_type: AssociationType, debug: bool = False +) -> List[str]: association_types = [] if association_type.drug_association: association_types.append("Drug") if debug: logger.debug(f"Drug Association: {association_type.drug_association}") - logger.debug(f"Drug Association Explanation: {association_type.drug_association_explanation}") - logger.debug(f"Drug Association Quote: {association_type.drug_association_quote}") + logger.debug( + f"Drug Association Explanation: {association_type.drug_association_explanation}" + ) + logger.debug( + f"Drug Association Quote: {association_type.drug_association_quote}" + ) if association_type.phenotype_association: association_types.append("Phenotype") if debug: - logger.debug(f"Phenotype Association: {association_type.phenotype_association}") - logger.debug(f"Phenotype Association Explanation: {association_type.phenotype_association_explanation}") - logger.debug(f"Phenotype Association Quote: {association_type.phenotype_association_quote}") + logger.debug( + f"Phenotype Association: {association_type.phenotype_association}" + ) + logger.debug( + f"Phenotype Association Explanation: {association_type.phenotype_association_explanation}" + ) + logger.debug( + f"Phenotype Association Quote: {association_type.phenotype_association_quote}" + ) if association_type.functional_association: association_types.append("Functional") if debug: - logger.debug(f"Functional Association: {association_type.functional_association}") - logger.debug(f"Functional Association Explanation: {association_type.functional_association_explanation}") - logger.debug(f"Functional Association Quote: {association_type.functional_association_quote}") - logger.info(f"Variant: {association_type.variant.variant_id} has association types: {association_types}") - return association_types \ No newline at end of file + logger.debug( + f"Functional Association: {association_type.functional_association}" + ) + logger.debug( + f"Functional Association Explanation: {association_type.functional_association_explanation}" + ) + logger.debug( + f"Functional Association Quote: {association_type.functional_association_quote}" + ) + logger.info( + f"Variant: {association_type.variant.variant_id} has association types: {association_types}" + ) + return association_types diff --git a/src/inference.py b/src/inference.py index 91fb0ff..b262ece 100644 --- a/src/inference.py +++ b/src/inference.py @@ -16,13 +16,15 @@ def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): self.model = model self.temperature = temperature - def prompted_generate(self, hydrated_prompt: HydratedPrompt, temperature: float = None) -> str: + def prompted_generate( + self, hydrated_prompt: HydratedPrompt, temperature: float = None + ) -> str: temp = temperature if temperature is not None else self.temperature return self.generate( - hydrated_prompt.input_prompt, - hydrated_prompt.system_prompt, - temp, - hydrated_prompt.output_format_structure + hydrated_prompt.input_prompt, + hydrated_prompt.system_prompt, + temp, + hydrated_prompt.output_format_structure, ) @abstractmethod @@ -101,9 +103,14 @@ def generate( {"role": "user", "content": prompt}, ] else: - logger.warning("No system prompt provided. Using default system prompt. System prompts recommended for parsing.") + logger.warning( + "No system prompt provided. Using default system prompt. System prompts recommended for parsing." + ) messages = [ - {"role": "system", "content": "Your job is to parse the response into a structured output. Please provide your response in the exact format specified by the response_format parameter."}, + { + "role": "system", + "content": "Your job is to parse the response into a structured output. Please provide your response in the exact format specified by the response_format parameter.", + }, {"role": "user", "content": prompt}, ] try: @@ -118,7 +125,6 @@ def generate( raise e return response.choices[0].message.content - class Variant(BaseModel): """Variant.""" @@ -133,6 +139,3 @@ class VariantList(BaseModel): """List of variants.""" variant_list: List[Variant] - - - diff --git a/src/prompts.py b/src/prompts.py index 0c5253d..24424f0 100644 --- a/src/prompts.py +++ b/src/prompts.py @@ -38,7 +38,9 @@ class PromptVariables(BaseModel): key_question: str output_queues: Optional[str] = None system_prompt: Optional[str] = None - output_format_structure: Optional[Union[Type[BaseModel], List[Type[BaseModel]]]] = None + output_format_structure: Optional[Union[Type[BaseModel], List[Type[BaseModel]]]] = ( + None + ) class HydratedPrompt(BaseModel): @@ -58,14 +60,22 @@ def hydrate_prompt(self) -> HydratedPrompt: """Hydrate the prompt.""" return HydratedPrompt( system_prompt=self.prompt_variables.system_prompt, - input_prompt=self.prompt_template.format(**self.prompt_variables.model_dump()), + input_prompt=self.prompt_template.format( + **self.prompt_variables.model_dump() + ), output_format_structure=self.prompt_variables.output_format_structure, ) -class ParserPrompt(): + +class ParserPrompt: """Parser prompt generator.""" - def __init__(self, input_prompt: str, output_format_structure: Type[BaseModel], system_prompt: str = None): + def __init__( + self, + input_prompt: str, + output_format_structure: Type[BaseModel], + system_prompt: str = None, + ): self.input_prompt = input_prompt self.output_format_structure = output_format_structure self.system_prompt = system_prompt @@ -84,4 +94,4 @@ def hydrate_prompt(self) -> HydratedPrompt: system_prompt=self.system_prompt, input_prompt=self.input_prompt, output_format_structure=self.output_format_structure, - ) \ No newline at end of file + ) diff --git a/src/utils.py b/src/utils.py index 41b841a..2a6aab0 100644 --- a/src/utils.py +++ b/src/utils.py @@ -6,6 +6,7 @@ from src.inference import Variant from src.article_parser import MarkdownParser + def extractVariantsRegex(text): # Note, seems to extract a ton of variants, not just the ones that are being studied # Think it might only be applicable to rsIDs @@ -22,10 +23,13 @@ def save_output(prompt, output, filename): f.write(output) logger.info(f"Saved output to {filename}.txt") -def compare_lists(experimental_list: List[str], ground_truth_list: List[str], pmcid: str): + +def compare_lists( + experimental_list: List[str], ground_truth_list: List[str], pmcid: str +): """ Compare experimental list with ground truth list and calculate performance metrics. - + Args: experimental_list (list): List of predicted/experimental values ground_truth_list (list): List of actual/ground truth values @@ -37,41 +41,42 @@ def compare_lists(experimental_list: List[str], ground_truth_list: List[str], pm # Convert lists to sets for efficient comparison experimental_set = set(experimental_list) ground_truth_set = set(ground_truth_list) - + # Calculate performance metrics true_positives = len(experimental_set.intersection(ground_truth_set)) false_positives = len(experimental_set - ground_truth_set) false_negatives = len(ground_truth_set - experimental_set) true_negatives = 0 # Not applicable in this context - + # Color-code the lists colored_experimental = [] colored_ground_truth = [] - + # Color experimental list for item in experimental_list: if item in ground_truth_set: - colored_experimental.append(colored(item, 'green')) + colored_experimental.append(colored(item, "green")) else: - colored_experimental.append(colored(item, 'red')) - + colored_experimental.append(colored(item, "red")) + # Color ground truth list for item in ground_truth_list: if item in experimental_set: - colored_ground_truth.append(colored(item, 'green')) + colored_ground_truth.append(colored(item, "green")) else: - colored_ground_truth.append(colored(item, 'red')) - + colored_ground_truth.append(colored(item, "red")) + # Print colored lists print(f"================= {pmcid} =================") print("Experimental List:") - print(' '.join(map(str, colored_experimental))) + print(" ".join(map(str, colored_experimental))) print("\nGround Truth List:") - print(' '.join(map(str, colored_ground_truth))) - + print(" ".join(map(str, colored_ground_truth))) + # Return performance metrics return true_positives, true_negatives, false_positives, false_negatives + def get_true_variants(pmcid): """ Get the actual annotated variants for a given PMCID. @@ -94,4 +99,4 @@ def get_article_text(pmcid: str = None, article_text: str = None): if article_text is None: article_text = MarkdownParser(pmcid=pmcid).get_article_text() - return article_text \ No newline at end of file + return article_text From 93b63fe0e3a7e77c5ffb39a611e02140ec02cf7a Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Fri, 27 Jun 2025 00:54:24 -0700 Subject: [PATCH 26/28] checkpoint: started step 1 pipeline --- notebooks/testing_components.ipynb | 223 +++++------------- src/components/all_variants.py | 3 +- src/components/association_types.py | 15 +- .../variant_association_pipeline.py | 155 ++++++++++++ src/inference.py | 15 -- src/utils.py | 4 - src/variants.py | 16 ++ 7 files changed, 247 insertions(+), 184 deletions(-) create mode 100644 src/components/variant_association_pipeline.py create mode 100644 src/variants.py diff --git a/notebooks/testing_components.ipynb b/notebooks/testing_components.ipynb index 23103d3..aae8a7b 100644 --- a/notebooks/testing_components.ipynb +++ b/notebooks/testing_components.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -117,203 +117,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-27 00:20:59.201\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-27 00:20:59.203\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-27 00:20:59.204\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mget_association_types\u001b[0m:\u001b[36m5\u001b[0m - \u001b[1mDetermining association type for variants [Variant(variant_id='rs2909451', gene='DPP4', allele='TT', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'), Variant(variant_id='rs4664443', gene='DPP4', allele='GG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'), Variant(variant_id='rs3765467', gene='GLP1R', allele='AG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'), Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'), Variant(variant_id='rs6923761', gene='GLP1R', allele='AA', evidence='Regarding GLP1R gene polymorphisms, patients with the rs6923761 AA homozygous genotype in the study group had a median HbA1c improvement of 0.90 (IQR, 0.61–1.01), while the control group showed 1.41 (IQR, 1.12–1.45; P = .010), suggesting reduced glycemic response to sitagliptin.'), Variant(variant_id='rs163184', gene='KCNQ1', allele='GG', evidence='KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group (P < .001), suggesting lower responsiveness to sitagliptin and better response to gliclazide.'), Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs7754840 CG genotype showed a median improvement of 1.44 (IQR, 1.38–1.72) in the study group compared with 1.09 (IQR, 0.79–1.17) in the control group (P = .053).'), Variant(variant_id='rs756992', gene='CDKAL1', allele='AG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs756992 AG genotype exhibited a median improvement of 1.43 (IQR, 1.28–1.52) in the study group compared with 1.10 (IQR, 0.87–1.18) in the control group (P = .081).'), Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT', evidence='CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).'), Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (P = .464).')]\u001b[0m\n" + "\u001b[32m2025-06-27 00:31:27.068\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-27 00:31:27.071\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-27 00:31:27.073\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mget_association_types\u001b[0m:\u001b[36m5\u001b[0m - \u001b[1mDetermining association type for variants ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs6923761', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n" ] } ], "source": [ - "association = get_association_types(variants, pmcid=\"PMC11730665\")" + "association_types = get_association_types(variants, pmcid=\"PMC11730665\")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 23:56:27.106\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:27.108\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:27.109\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs2909451\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:39.521\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs2909451 has association types: ['Drug', 'Phenotype']\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:39.523\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:39.526\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:39.529\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs4664443\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug', 'Phenotype']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-26 23:56:48.218\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs4664443 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:48.219\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:48.221\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:56:48.222\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs3765467\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-26 23:57:03.351\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs3765467 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:03.353\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:03.355\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:03.356\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs2285676\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-26 23:57:17.459\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs2285676 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:17.461\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:17.463\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:17.465\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs6923761\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-26 23:57:36.520\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs6923761 has association types: ['Drug', 'Phenotype']\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:36.524\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:36.527\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:36.530\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs163184\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug', 'Phenotype']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-26 23:57:53.632\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs163184 has association types: ['Drug', 'Phenotype']\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:53.634\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:53.636\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:57:53.637\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs7754840\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug', 'Phenotype']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-26 23:58:03.136\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs7754840 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:03.139\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:03.141\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:03.141\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs756992\u001b[0m\n" + "\u001b[32m2025-06-27 00:32:40.312\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs2909451 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs4664443 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs3765467 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs2285676 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs6923761 has association types: ['Drug', 'Phenotype']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs163184 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs7754840 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs756992 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs1799853 has association types: ['Drug']\u001b[0m\n", + "\u001b[32m2025-06-27 00:32:40.315\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs1057910 has association types: ['Drug']\u001b[0m\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-26 23:58:11.293\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs756992 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:11.297\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:11.301\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:11.305\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs1799853\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug']\n" - ] - }, + } + ], + "source": [ + "for association_type in association_types:\n", + " list_association_types(association_type)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 23:58:21.112\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs1799853 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:21.114\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:21.116\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:58:21.118\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mdetermine_association_type\u001b[0m:\u001b[36m4\u001b[0m - \u001b[1mDetermining association type for variant rs1057910\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug']\n" + "\u001b[32m2025-06-27 00:51:41.232\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 23:58:33.535\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mVariant: rs1057910 has association types: ['Drug', 'Functional']\u001b[0m\n" + "\u001b[32m2025-06-27 00:51:41.234\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-27 00:51:41.235\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mStep 1: Extracting variants from article\u001b[0m\n", + "\u001b[32m2025-06-27 00:51:41.236\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msrc.utils\u001b[0m:\u001b[36mget_article_text\u001b[0m:\u001b[36m96\u001b[0m - \u001b[33m\u001b[1mBoth article_text and PMCID are provided. Using article_text.\u001b[0m\n", + "\u001b[32m2025-06-27 00:51:41.237\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n", + "\u001b[32m2025-06-27 00:52:00.050\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m63\u001b[0m - \u001b[1mFound 10 variants\u001b[0m\n", + "\u001b[32m2025-06-27 00:52:00.051\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m51\u001b[0m - \u001b[1mExtracted 10 variants: ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs6923761', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n", + "\u001b[32m2025-06-27 00:52:00.051\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m62\u001b[0m - \u001b[1mStep 2: Determining association types for variants\u001b[0m\n", + "\u001b[32m2025-06-27 00:52:00.052\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msrc.utils\u001b[0m:\u001b[36mget_article_text\u001b[0m:\u001b[36m96\u001b[0m - \u001b[33m\u001b[1mBoth article_text and PMCID are provided. Using article_text.\u001b[0m\n", + "\u001b[32m2025-06-27 00:52:00.052\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mget_association_types\u001b[0m:\u001b[36m107\u001b[0m - \u001b[1mDetermining association type for variants ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs6923761', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n", + "\u001b[32m2025-06-27 00:53:18.632\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m74\u001b[0m - \u001b[1mStep 3: Categorizing variants by association type\u001b[0m\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Drug', 'Functional']\n" + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'association_types'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcomponents\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvariant_association_pipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m run_variant_association_pipeline\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mrun_variant_association_pipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPMC11730665\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/components/variant_association_pipeline.py:153\u001b[39m, in \u001b[36mrun_variant_association_pipeline\u001b[39m\u001b[34m(article_text, pmcid, model, temperature)\u001b[39m\n\u001b[32m 140\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 141\u001b[39m \u001b[33;03mConvenience function to run the variant association pipeline.\u001b[39;00m\n\u001b[32m 142\u001b[39m \u001b[33;03m\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 150\u001b[39m \u001b[33;03m Dictionary with lists of variants for each association type\u001b[39;00m\n\u001b[32m 151\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 152\u001b[39m pipeline = VariantAssociationPipeline(model=model, temperature=temperature)\n\u001b[32m--> \u001b[39m\u001b[32m153\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpipeline\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprocess_article\u001b[49m\u001b[43m(\u001b[49m\u001b[43marticle_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43marticle_text\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/components/variant_association_pipeline.py:75\u001b[39m, in \u001b[36mVariantAssociationPipeline.process_article\u001b[39m\u001b[34m(self, article_text, pmcid)\u001b[39m\n\u001b[32m 73\u001b[39m \u001b[38;5;66;03m# Step 3: Categorize variants by association type\u001b[39;00m\n\u001b[32m 74\u001b[39m logger.info(\u001b[33m\"\u001b[39m\u001b[33mStep 3: Categorizing variants by association type\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m75\u001b[39m result = \u001b[38;5;28mself\u001b[39m._categorize_variants(variants, \u001b[43massociation_types_result\u001b[49m\u001b[43m.\u001b[49m\u001b[43massociation_types\u001b[49m)\n\u001b[32m 77\u001b[39m logger.info(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFinal categorization: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(result[\u001b[33m'\u001b[39m\u001b[33mdrug_associations\u001b[39m\u001b[33m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m drug, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 78\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(result[\u001b[33m'\u001b[39m\u001b[33mphenotype_associations\u001b[39m\u001b[33m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m phenotype, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 79\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(result[\u001b[33m'\u001b[39m\u001b[33mfunctional_associations\u001b[39m\u001b[33m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m functional associations\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "\u001b[31mAttributeError\u001b[39m: 'list' object has no attribute 'association_types'" ] } ], "source": [ - "for variant in variants:\n", - " association = determine_association_type(variant, pmcid=\"PMC11730665\")\n", - " print(list_association_types(association))" + "from src.components.variant_association_pipeline import run_variant_association_pipeline\n", + "\n", + "run_variant_association_pipeline(pmcid=\"PMC11730665\")" ] }, { diff --git a/src/components/all_variants.py b/src/components/all_variants.py index 02c3760..42941a4 100644 --- a/src/components/all_variants.py +++ b/src/components/all_variants.py @@ -1,4 +1,5 @@ -from src.inference import Variant, VariantList, Generator +from src.inference import Generator +from src.variants import Variant, VariantList from src.prompts import GeneratorPrompt, PromptVariables from src.utils import get_article_text from loguru import logger diff --git a/src/components/association_types.py b/src/components/association_types.py index ef793db..7d946f1 100644 --- a/src/components/association_types.py +++ b/src/components/association_types.py @@ -2,7 +2,7 @@ Given a list of variants and the article text, determine the type of association (drug, phenotype, functional association) """ -from src.components.all_variants import Variant +from src.variants import Variant from typing import List from src.prompts import PromptVariables, GeneratorPrompt, ParserPrompt from src.inference import Generator, Parser @@ -13,7 +13,18 @@ class AssociationType(BaseModel): """ - Association type. + Variant Association Type + Members: + - variant: Variant + - drug_association: bool + - drug_association_explanation: str + - drug_association_quote: str + - phenotype_association: bool + - phenotype_association_explanation: str + - phenotype_association_quote: str + - functional_association: bool + - functional_association_explanation: str + - functional_association_quote: str """ variant: Variant diff --git a/src/components/variant_association_pipeline.py b/src/components/variant_association_pipeline.py new file mode 100644 index 0000000..4ae80d8 --- /dev/null +++ b/src/components/variant_association_pipeline.py @@ -0,0 +1,155 @@ +""" +For an article +1. Extract all variants +2. Determine the association type for each variant + +Final output: +- Dictionary of +{ +"drug_associations": List[Variant], +"phenotype_associations": List[Variant], +"functional_associations": List[Variant], +} +""" + +from typing import Dict, List, Optional +from loguru import logger +from src.components.all_variants import extract_all_variants +from src.components.association_types import get_association_types, AssociationType +from src.utils import get_article_text +from src.variants import Variant + + +class VariantAssociationPipeline: + """Pipeline to extract variants and determine their association types from an article.""" + + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): + self.model = model + self.temperature = temperature + + def process_article( + self, + article_text: str = None, + pmcid: str = None + ) -> Dict[str, List[Variant]]: + """ + Process an article to extract variants and determine their association types. + + Args: + article_text: The text of the article + pmcid: The PMCID of the article + + Returns: + Dictionary with lists of variants for each association type + """ + # Get article text + article_text = get_article_text(pmcid=pmcid, article_text=article_text) + + # Step 1: Extract all variants + logger.info("Step 1: Extracting variants from article") + variants = extract_all_variants(article_text, pmcid) + logger.info(f"Extracted {len(variants)} variants: {[v.variant_id for v in variants]}") + + if not variants: + logger.warning("No variants found in article") + return { + "drug_associations": [], + "phenotype_associations": [], + "functional_associations": [] + } + + # Step 2: Determine association types for all variants + logger.info("Step 2: Determining association types for variants") + association_types_result = get_association_types(variants, article_text, pmcid) + + if association_types_result is None: + logger.error("Failed to determine association types") + return { + "drug_associations": [], + "phenotype_associations": [], + "functional_associations": [] + } + + # Step 3: Categorize variants by association type + logger.info("Step 3: Categorizing variants by association type") + result = self._categorize_variants(variants, association_types_result.association_types) + + logger.info(f"Final categorization: {len(result['drug_associations'])} drug, " + f"{len(result['phenotype_associations'])} phenotype, " + f"{len(result['functional_associations'])} functional associations") + + return result + + def _categorize_variants( + self, + variants: List[Variant], + association_types: List[AssociationType] + ) -> Dict[str, List[Variant]]: + """ + Categorize variants based on their association types. + + Args: + variants: List of variants + association_types: List of association type results + + Returns: + Dictionary with variants categorized by association type + """ + drug_associations = [] + phenotype_associations = [] + functional_associations = [] + + # Create a mapping from variant_id to association_type for easy lookup + variant_to_association = { + assoc.variant.variant_id: assoc for assoc in association_types + } + + for variant in variants: + association = variant_to_association.get(variant.variant_id) + + if association is None: + logger.warning(f"No association type found for variant {variant.variant_id}") + continue + + # Categorize based on association types + if association.drug_association: + drug_associations.append(variant) + logger.debug(f"Variant {variant.variant_id} has drug association: {association.drug_association_explanation}") + + if association.phenotype_association: + phenotype_associations.append(variant) + logger.debug(f"Variant {variant.variant_id} has phenotype association: {association.phenotype_association_explanation}") + + if association.functional_association: + functional_associations.append(variant) + logger.debug(f"Variant {variant.variant_id} has functional association: {association.functional_association_explanation}") + + return { + "drug_associations": drug_associations, + "phenotype_associations": phenotype_associations, + "functional_associations": functional_associations + } + + +def run_variant_association_pipeline( + article_text: str = None, + pmcid: str = None, + model: str = "gpt-4o-mini", + temperature: float = 0.1 +) -> Dict[str, List[Variant]]: + """ + Convenience function to run the variant association pipeline. + + Args: + article_text: The text of the article + pmcid: The PMCID of the article + model: The LLM model to use + temperature: The temperature for LLM generation + + Returns: + Dictionary with lists of variants for each association type + """ + pipeline = VariantAssociationPipeline(model=model, temperature=temperature) + return pipeline.process_article(article_text=article_text, pmcid=pmcid) + + \ No newline at end of file diff --git a/src/inference.py b/src/inference.py index b262ece..95b7631 100644 --- a/src/inference.py +++ b/src/inference.py @@ -124,18 +124,3 @@ def generate( logger.error(f"Error generating response: {e}") raise e return response.choices[0].message.content - - -class Variant(BaseModel): - """Variant.""" - - variant_id: str - gene: str | None = None - allele: str | None = None - evidence: str | None = None - - -class VariantList(BaseModel): - """List of variants.""" - - variant_list: List[Variant] diff --git a/src/utils.py b/src/utils.py index 2a6aab0..4e88fe0 100644 --- a/src/utils.py +++ b/src/utils.py @@ -3,7 +3,6 @@ import json from typing import List from termcolor import colored -from src.inference import Variant from src.article_parser import MarkdownParser @@ -93,9 +92,6 @@ def get_article_text(pmcid: str = None, article_text: str = None): logger.error("Either article_text or pmcid must be provided.") raise ValueError("Either article_text or pmcid must be provided.") - if article_text is not None and pmcid is not None: - logger.warning("Both article_text and PMCID are provided. Using article_text.") - if article_text is None: article_text = MarkdownParser(pmcid=pmcid).get_article_text() diff --git a/src/variants.py b/src/variants.py new file mode 100644 index 0000000..9610883 --- /dev/null +++ b/src/variants.py @@ -0,0 +1,16 @@ +from pydantic import BaseModel +from typing import List + +class Variant(BaseModel): + """Variant.""" + + variant_id: str + gene: str | None = None + allele: str | None = None + evidence: str | None = None + + +class VariantList(BaseModel): + """List of variants.""" + + variant_list: List[Variant] \ No newline at end of file From e79870b7609139d5cba0fa9db722f6adf513a876 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Sat, 28 Jun 2025 09:38:39 -0700 Subject: [PATCH 27/28] feat: config with global debug flag --- notebooks/testing_components.ipynb | 248 +++++++++++++----- src/components/all_variants.py | 8 +- src/components/association_types.py | 10 +- .../variant_association_pipeline.py | 12 +- src/config.py | 55 ++++ 5 files changed, 254 insertions(+), 79 deletions(-) create mode 100644 src/config.py diff --git a/notebooks/testing_components.ipynb b/notebooks/testing_components.ipynb index aae8a7b..e679d1f 100644 --- a/notebooks/testing_components.ipynb +++ b/notebooks/testing_components.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# Run this cell: \n", "# The lines below will instruct jupyter to reload imported modules before \n", @@ -18,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -39,17 +48,23 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-26 23:42:47.357\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", - "\u001b[32m2025-06-26 23:42:47.358\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-26 23:42:47.359\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n", - "\u001b[32m2025-06-26 23:43:01.195\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m26\u001b[0m - \u001b[1mFound 10 variants\u001b[0m\n" + "\u001b[32m2025-06-27 09:01:58.250\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-27 09:01:58.253\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-27 09:01:58.258\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-06-27 09:02:16.084\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m63\u001b[0m - \u001b[1mFound 9 variants\u001b[0m\n" ] } ], @@ -61,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -71,15 +86,14 @@ " Variant(variant_id='rs4664443', gene='DPP4', allele='GG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", - " Variant(variant_id='rs6923761', gene='GLP1R', allele='AA', evidence='Regarding GLP1R gene polymorphisms, patients with the rs6923761 AA homozygous genotype in the study group had a median HbA1c improvement of 0.90 (IQR, 0.61–1.01), while the control group showed 1.41 (IQR, 1.12–1.45; P = .010), suggesting reduced glycemic response to sitagliptin.'),\n", - " Variant(variant_id='rs163184', gene='KCNQ1', allele='GG', evidence='KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group (P < .001), suggesting lower responsiveness to sitagliptin and better response to gliclazide.'),\n", - " Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs7754840 CG genotype showed a median improvement of 1.44 (IQR, 1.38–1.72) in the study group compared with 1.09 (IQR, 0.79–1.17) in the control group (P = .053).'),\n", - " Variant(variant_id='rs756992', gene='CDKAL1', allele='AG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs756992 AG genotype exhibited a median improvement of 1.43 (IQR, 1.28–1.52) in the study group compared with 1.10 (IQR, 0.87–1.18) in the control group (P = .081).'),\n", - " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT', evidence='CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).'),\n", - " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (P = .464).')]" + " Variant(variant_id='rs163184', gene='KCNQ1', allele='GG', evidence='KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group (*P* < .001), suggesting lower responsiveness to sitagliptin and better response to gliclazide.'),\n", + " Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs7754840 CG genotype showed a median improvement of 1.44 (IQR, 1.38–1.72) in the study group compared with 1.09 (IQR, 0.79–1.17) in the control group (*P* = .053).'),\n", + " Variant(variant_id='rs756992', gene='CDKAL1', allele='AG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs756992 AG genotype exhibited a median improvement of 1.43 (IQR, 1.28–1.52) in the study group compared with 1.10 (IQR, 0.87–1.18) in the control group (*P* = .081).'),\n", + " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT', evidence='CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; *P* < .001).'),\n", + " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (*P* = .464).')]" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -90,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -108,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -117,22 +131,61 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-27 00:31:27.068\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n" + "\u001b[32m2025-06-27 09:02:24.739\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-27 09:02:24.740\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-27 00:31:27.071\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-27 00:31:27.073\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mget_association_types\u001b[0m:\u001b[36m5\u001b[0m - \u001b[1mDetermining association type for variants ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs6923761', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n" + "\u001b[32m2025-06-27 09:02:24.741\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mget_association_types\u001b[0m:\u001b[36m107\u001b[0m - \u001b[1mDetermining association type for variants ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m association_types = \u001b[43mget_association_types\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPMC11730665\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/components/association_types.py:122\u001b[39m, in \u001b[36mget_association_types\u001b[39m\u001b[34m(variants, article_text, pmcid)\u001b[39m\n\u001b[32m 116\u001b[39m parser = Parser(model=\u001b[33m\"\u001b[39m\u001b[33mgpt-4o-mini\u001b[39m\u001b[33m\"\u001b[39m, temperature=\u001b[32m0.1\u001b[39m)\n\u001b[32m 117\u001b[39m parser_prompt = ParserPrompt(\n\u001b[32m 118\u001b[39m input_prompt=response,\n\u001b[32m 119\u001b[39m output_format_structure=AssociationTypeList,\n\u001b[32m 120\u001b[39m system_prompt=generator_prompt.system_prompt,\n\u001b[32m 121\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m122\u001b[39m parsed_response = \u001b[43mparser\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprompted_generate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparser_prompt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 124\u001b[39m \u001b[38;5;66;03m# Parse the string response into AssociationType objects\u001b[39;00m\n\u001b[32m 125\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/inference.py:23\u001b[39m, in \u001b[36mLLMInterface.prompted_generate\u001b[39m\u001b[34m(self, hydrated_prompt, temperature)\u001b[39m\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mprompted_generate\u001b[39m(\n\u001b[32m 20\u001b[39m \u001b[38;5;28mself\u001b[39m, hydrated_prompt: HydratedPrompt, temperature: \u001b[38;5;28mfloat\u001b[39m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 21\u001b[39m ) -> \u001b[38;5;28mstr\u001b[39m:\n\u001b[32m 22\u001b[39m temp = temperature \u001b[38;5;28;01mif\u001b[39;00m temperature \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m.temperature\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 24\u001b[39m \u001b[43m \u001b[49m\u001b[43mhydrated_prompt\u001b[49m\u001b[43m.\u001b[49m\u001b[43minput_prompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 25\u001b[39m \u001b[43m \u001b[49m\u001b[43mhydrated_prompt\u001b[49m\u001b[43m.\u001b[49m\u001b[43msystem_prompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 26\u001b[39m \u001b[43m \u001b[49m\u001b[43mtemp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 27\u001b[39m \u001b[43m \u001b[49m\u001b[43mhydrated_prompt\u001b[49m\u001b[43m.\u001b[49m\u001b[43moutput_format_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 28\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/inference.py:117\u001b[39m, in \u001b[36mParser.generate\u001b[39m\u001b[34m(self, prompt, system_prompt, temperature, response_format)\u001b[39m\n\u001b[32m 109\u001b[39m messages = [\n\u001b[32m 110\u001b[39m {\n\u001b[32m 111\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mrole\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33msystem\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 114\u001b[39m {\u001b[33m\"\u001b[39m\u001b[33mrole\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33muser\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mcontent\u001b[39m\u001b[33m\"\u001b[39m: prompt},\n\u001b[32m 115\u001b[39m ]\n\u001b[32m 116\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m117\u001b[39m response = \u001b[43mlitellm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompletion\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 118\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 119\u001b[39m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 120\u001b[39m \u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 121\u001b[39m \u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtemp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 122\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 124\u001b[39m logger.error(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError generating response: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/litellm/utils.py:1163\u001b[39m, in \u001b[36mclient..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 1161\u001b[39m print_verbose(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError while checking max token limit: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 1162\u001b[39m \u001b[38;5;66;03m# MODEL CALL\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1163\u001b[39m result = \u001b[43moriginal_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1164\u001b[39m end_time = datetime.datetime.now()\n\u001b[32m 1165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mstream\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m kwargs[\u001b[33m\"\u001b[39m\u001b[33mstream\u001b[39m\u001b[33m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/litellm/main.py:1838\u001b[39m, in \u001b[36mcompletion\u001b[39m\u001b[34m(model, messages, timeout, temperature, top_p, n, stream, stream_options, stop, max_completion_tokens, max_tokens, modalities, prediction, audio, presence_penalty, frequency_penalty, logit_bias, user, reasoning_effort, response_format, seed, tools, tool_choice, logprobs, top_logprobs, parallel_tool_calls, web_search_options, deployment_id, extra_headers, functions, function_call, base_url, api_version, api_key, model_list, thinking, **kwargs)\u001b[39m\n\u001b[32m 1836\u001b[39m \u001b[38;5;66;03m## COMPLETION CALL\u001b[39;00m\n\u001b[32m 1837\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1838\u001b[39m response = \u001b[43mopenai_chat_completions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompletion\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1839\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1840\u001b[39m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1841\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1842\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel_response\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel_response\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1843\u001b[39m \u001b[43m \u001b[49m\u001b[43mprint_verbose\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprint_verbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1844\u001b[39m \u001b[43m \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[43m=\u001b[49m\u001b[43mapi_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1845\u001b[39m \u001b[43m \u001b[49m\u001b[43mapi_base\u001b[49m\u001b[43m=\u001b[49m\u001b[43mapi_base\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1846\u001b[39m \u001b[43m \u001b[49m\u001b[43macompletion\u001b[49m\u001b[43m=\u001b[49m\u001b[43macompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1847\u001b[39m \u001b[43m \u001b[49m\u001b[43mlogging_obj\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlogging\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1848\u001b[39m \u001b[43m \u001b[49m\u001b[43moptional_params\u001b[49m\u001b[43m=\u001b[49m\u001b[43moptional_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1849\u001b[39m \u001b[43m \u001b[49m\u001b[43mlitellm_params\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlitellm_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1850\u001b[39m \u001b[43m \u001b[49m\u001b[43mlogger_fn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlogger_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1851\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[32m 1852\u001b[39m \u001b[43m \u001b[49m\u001b[43mcustom_prompt_dict\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcustom_prompt_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1853\u001b[39m \u001b[43m \u001b[49m\u001b[43mclient\u001b[49m\u001b[43m=\u001b[49m\u001b[43mclient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# pass AsyncOpenAI, OpenAI client\u001b[39;49;00m\n\u001b[32m 1854\u001b[39m \u001b[43m \u001b[49m\u001b[43morganization\u001b[49m\u001b[43m=\u001b[49m\u001b[43morganization\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1855\u001b[39m \u001b[43m \u001b[49m\u001b[43mcustom_llm_provider\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcustom_llm_provider\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1856\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1857\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 1858\u001b[39m \u001b[38;5;66;03m## LOGGING - log the original exception returned\u001b[39;00m\n\u001b[32m 1859\u001b[39m logging.post_call(\n\u001b[32m 1860\u001b[39m \u001b[38;5;28minput\u001b[39m=messages,\n\u001b[32m 1861\u001b[39m api_key=api_key,\n\u001b[32m 1862\u001b[39m original_response=\u001b[38;5;28mstr\u001b[39m(e),\n\u001b[32m 1863\u001b[39m additional_args={\u001b[33m\"\u001b[39m\u001b[33mheaders\u001b[39m\u001b[33m\"\u001b[39m: headers},\n\u001b[32m 1864\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/litellm/llms/openai/openai.py:653\u001b[39m, in \u001b[36mOpenAIChatCompletion.completion\u001b[39m\u001b[34m(self, model_response, timeout, optional_params, litellm_params, logging_obj, model, messages, print_verbose, api_key, api_base, api_version, dynamic_params, azure_ad_token, acompletion, logger_fn, headers, custom_prompt_dict, client, organization, custom_llm_provider, drop_params)\u001b[39m\n\u001b[32m 638\u001b[39m \u001b[38;5;66;03m## LOGGING\u001b[39;00m\n\u001b[32m 639\u001b[39m logging_obj.pre_call(\n\u001b[32m 640\u001b[39m \u001b[38;5;28minput\u001b[39m=messages,\n\u001b[32m 641\u001b[39m api_key=openai_client.api_key,\n\u001b[32m (...)\u001b[39m\u001b[32m 647\u001b[39m },\n\u001b[32m 648\u001b[39m )\n\u001b[32m 650\u001b[39m (\n\u001b[32m 651\u001b[39m headers,\n\u001b[32m 652\u001b[39m response,\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m ) = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmake_sync_openai_chat_completion_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mopenai_client\u001b[49m\u001b[43m=\u001b[49m\u001b[43mopenai_client\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 656\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 657\u001b[39m \u001b[43m \u001b[49m\u001b[43mlogging_obj\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlogging_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 658\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 660\u001b[39m logging_obj.model_call_details[\u001b[33m\"\u001b[39m\u001b[33mresponse_headers\u001b[39m\u001b[33m\"\u001b[39m] = headers\n\u001b[32m 661\u001b[39m stringified_response = response.model_dump()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/litellm/litellm_core_utils/logging_utils.py:149\u001b[39m, in \u001b[36mtrack_llm_api_timing..decorator..sync_wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 147\u001b[39m start_time = datetime.now()\n\u001b[32m 148\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m149\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 150\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[32m 151\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/litellm/llms/openai/openai.py:453\u001b[39m, in \u001b[36mOpenAIChatCompletion.make_sync_openai_chat_completion_request\u001b[39m\u001b[34m(self, openai_client, data, timeout, logging_obj)\u001b[39m\n\u001b[32m 451\u001b[39m raw_response = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 452\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m453\u001b[39m raw_response = \u001b[43mopenai_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchat\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompletions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwith_raw_response\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 454\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 455\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 457\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(raw_response, \u001b[33m\"\u001b[39m\u001b[33mheaders\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 458\u001b[39m headers = \u001b[38;5;28mdict\u001b[39m(raw_response.headers)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/openai/_legacy_response.py:364\u001b[39m, in \u001b[36mto_raw_response_wrapper..wrapped\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 360\u001b[39m extra_headers[RAW_RESPONSE_HEADER] = \u001b[33m\"\u001b[39m\u001b[33mtrue\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 362\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mextra_headers\u001b[39m\u001b[33m\"\u001b[39m] = extra_headers\n\u001b[32m--> \u001b[39m\u001b[32m364\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(LegacyAPIResponse[R], \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/openai/_utils/_utils.py:287\u001b[39m, in \u001b[36mrequired_args..inner..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 285\u001b[39m msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[32m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 286\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m287\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/openai/resources/chat/completions/completions.py:925\u001b[39m, in \u001b[36mCompletions.create\u001b[39m\u001b[34m(self, messages, model, audio, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, reasoning_effort, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, web_search_options, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m 882\u001b[39m \u001b[38;5;129m@required_args\u001b[39m([\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m\"\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstream\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 883\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\n\u001b[32m 884\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 922\u001b[39m timeout: \u001b[38;5;28mfloat\u001b[39m | httpx.Timeout | \u001b[38;5;28;01mNone\u001b[39;00m | NotGiven = NOT_GIVEN,\n\u001b[32m 923\u001b[39m ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[32m 924\u001b[39m validate_response_format(response_format)\n\u001b[32m--> \u001b[39m\u001b[32m925\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 926\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/chat/completions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 927\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 928\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 929\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m 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\u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mCompletionCreateParamsNonStreaming\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 964\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 965\u001b[39m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 966\u001b[39m \u001b[43m \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 967\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 968\u001b[39m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 969\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 970\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 971\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/openai/_base_client.py:1239\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m 1225\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m 1226\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1227\u001b[39m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1234\u001b[39m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1235\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m 1236\u001b[39m opts = FinalRequestOptions.construct(\n\u001b[32m 1237\u001b[39m method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m 1238\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1239\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/openai/_base_client.py:969\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m 967\u001b[39m response = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 968\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m969\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 970\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 971\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_should_stream_response_body\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 972\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 973\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 974\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m httpx.TimeoutException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m 975\u001b[39m log.debug(\u001b[33m\"\u001b[39m\u001b[33mEncountered httpx.TimeoutException\u001b[39m\u001b[33m\"\u001b[39m, exc_info=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpx/_client.py:914\u001b[39m, in \u001b[36mClient.send\u001b[39m\u001b[34m(self, request, stream, auth, follow_redirects)\u001b[39m\n\u001b[32m 910\u001b[39m \u001b[38;5;28mself\u001b[39m._set_timeout(request)\n\u001b[32m 912\u001b[39m auth = \u001b[38;5;28mself\u001b[39m._build_request_auth(request, auth)\n\u001b[32m--> \u001b[39m\u001b[32m914\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 915\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 916\u001b[39m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 917\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 918\u001b[39m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 919\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 920\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 921\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpx/_client.py:942\u001b[39m, in \u001b[36mClient._send_handling_auth\u001b[39m\u001b[34m(self, request, auth, follow_redirects, history)\u001b[39m\n\u001b[32m 939\u001b[39m request = \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[32m 941\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m942\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 943\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 944\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 945\u001b[39m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 946\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 947\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 948\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpx/_client.py:979\u001b[39m, in \u001b[36mClient._send_handling_redirects\u001b[39m\u001b[34m(self, request, follow_redirects, history)\u001b[39m\n\u001b[32m 976\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mrequest\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m 977\u001b[39m hook(request)\n\u001b[32m--> \u001b[39m\u001b[32m979\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 980\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 981\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mresponse\u001b[39m\u001b[33m\"\u001b[39m]:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpx/_client.py:1014\u001b[39m, in \u001b[36mClient._send_single_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 1009\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 1010\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1011\u001b[39m )\n\u001b[32m 1013\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request=request):\n\u001b[32m-> \u001b[39m\u001b[32m1014\u001b[39m response = \u001b[43mtransport\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1016\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, SyncByteStream)\n\u001b[32m 1018\u001b[39m response.request = request\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpx/_transports/default.py:250\u001b[39m, in \u001b[36mHTTPTransport.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 237\u001b[39m req = httpcore.Request(\n\u001b[32m 238\u001b[39m method=request.method,\n\u001b[32m 239\u001b[39m url=httpcore.URL(\n\u001b[32m (...)\u001b[39m\u001b[32m 247\u001b[39m extensions=request.extensions,\n\u001b[32m 248\u001b[39m )\n\u001b[32m 249\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[32m--> \u001b[39m\u001b[32m250\u001b[39m resp = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp.stream, typing.Iterable)\n\u001b[32m 254\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[32m 255\u001b[39m status_code=resp.status,\n\u001b[32m 256\u001b[39m headers=resp.headers,\n\u001b[32m 257\u001b[39m stream=ResponseStream(resp.stream),\n\u001b[32m 258\u001b[39m extensions=resp.extensions,\n\u001b[32m 259\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpcore/_sync/connection_pool.py:256\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 253\u001b[39m closing = \u001b[38;5;28mself\u001b[39m._assign_requests_to_connections()\n\u001b[32m 255\u001b[39m \u001b[38;5;28mself\u001b[39m._close_connections(closing)\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 258\u001b[39m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[32m 259\u001b[39m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n\u001b[32m 260\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, typing.Iterable)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpcore/_sync/connection_pool.py:236\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 232\u001b[39m connection = pool_request.wait_for_connection(timeout=timeout)\n\u001b[32m 234\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 235\u001b[39m \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m236\u001b[39m response = \u001b[43mconnection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 237\u001b[39m \u001b[43m \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[32m 240\u001b[39m \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[32m 241\u001b[39m \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[32m 242\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n\u001b[32m 244\u001b[39m pool_request.clear_connection()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpcore/_sync/connection.py:103\u001b[39m, in \u001b[36mHTTPConnection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 100\u001b[39m \u001b[38;5;28mself\u001b[39m._connect_failed = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_connection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpcore/_sync/http11.py:136\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[33m\"\u001b[39m\u001b[33mresponse_closed\u001b[39m\u001b[33m\"\u001b[39m, logger, request) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m 135\u001b[39m \u001b[38;5;28mself\u001b[39m._response_closed()\n\u001b[32m--> \u001b[39m\u001b[32m136\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpcore/_sync/http11.py:106\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 97\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\n\u001b[32m 98\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mreceive_response_headers\u001b[39m\u001b[33m\"\u001b[39m, logger, request, kwargs\n\u001b[32m 99\u001b[39m ) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m 100\u001b[39m (\n\u001b[32m 101\u001b[39m http_version,\n\u001b[32m 102\u001b[39m status,\n\u001b[32m 103\u001b[39m reason_phrase,\n\u001b[32m 104\u001b[39m headers,\n\u001b[32m 105\u001b[39m trailing_data,\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m ) = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_response_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 107\u001b[39m trace.return_value = (\n\u001b[32m 108\u001b[39m http_version,\n\u001b[32m 109\u001b[39m status,\n\u001b[32m 110\u001b[39m reason_phrase,\n\u001b[32m 111\u001b[39m headers,\n\u001b[32m 112\u001b[39m )\n\u001b[32m 114\u001b[39m network_stream = \u001b[38;5;28mself\u001b[39m._network_stream\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpcore/_sync/http11.py:177\u001b[39m, in \u001b[36mHTTP11Connection._receive_response_headers\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 174\u001b[39m timeout = timeouts.get(\u001b[33m\"\u001b[39m\u001b[33mread\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 176\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m177\u001b[39m event = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 178\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(event, h11.Response):\n\u001b[32m 179\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpcore/_sync/http11.py:217\u001b[39m, in \u001b[36mHTTP11Connection._receive_event\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 214\u001b[39m event = \u001b[38;5;28mself\u001b[39m._h11_state.next_event()\n\u001b[32m 216\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m event \u001b[38;5;129;01mis\u001b[39;00m h11.NEED_DATA:\n\u001b[32m--> \u001b[39m\u001b[32m217\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_network_stream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 218\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mREAD_NUM_BYTES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 219\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 221\u001b[39m \u001b[38;5;66;03m# If we feed this case through h11 we'll raise an exception like:\u001b[39;00m\n\u001b[32m 222\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 223\u001b[39m \u001b[38;5;66;03m# httpcore.RemoteProtocolError: can't handle event type\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 227\u001b[39m \u001b[38;5;66;03m# perspective. Instead we handle this case distinctly and treat\u001b[39;00m\n\u001b[32m 228\u001b[39m \u001b[38;5;66;03m# it as a ConnectError.\u001b[39;00m\n\u001b[32m 229\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data == \u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._h11_state.their_state == h11.SEND_RESPONSE:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/site-packages/httpcore/_backends/sync.py:128\u001b[39m, in \u001b[36mSyncStream.read\u001b[39m\u001b[34m(self, max_bytes, timeout)\u001b[39m\n\u001b[32m 126\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[32m 127\u001b[39m \u001b[38;5;28mself\u001b[39m._sock.settimeout(timeout)\n\u001b[32m--> \u001b[39m\u001b[32m128\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sock\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrecv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_bytes\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/ssl.py:1285\u001b[39m, in \u001b[36mSSLSocket.recv\u001b[39m\u001b[34m(self, buflen, flags)\u001b[39m\n\u001b[32m 1281\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m flags != \u001b[32m0\u001b[39m:\n\u001b[32m 1282\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 1283\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mnon-zero flags not allowed in calls to recv() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m %\n\u001b[32m 1284\u001b[39m \u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1285\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuflen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1286\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1287\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().recv(buflen, flags)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/.pixi/envs/default/lib/python3.13/ssl.py:1140\u001b[39m, in \u001b[36mSSLSocket.read\u001b[39m\u001b[34m(self, len, buffer)\u001b[39m\n\u001b[32m 1138\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._sslobj.read(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[32m 1139\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1140\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sslobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 1141\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m SSLError \u001b[38;5;28;01mas\u001b[39;00m x:\n\u001b[32m 1142\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m x.args[\u001b[32m0\u001b[39m] == SSL_ERROR_EOF \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.suppress_ragged_eofs:\n", + "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], @@ -142,23 +195,18 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 9, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-27 00:32:40.312\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs2909451 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs4664443 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs3765467 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs2285676 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs6923761 has association types: ['Drug', 'Phenotype']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs163184 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs7754840 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs756992 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs1799853 has association types: ['Drug']\u001b[0m\n", - "\u001b[32m2025-06-27 00:32:40.315\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mlist_association_types\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mVariant: rs1057910 has association types: ['Drug']\u001b[0m\n" + "ename": "NameError", + "evalue": "name 'association_types' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m association_type \u001b[38;5;129;01min\u001b[39;00m \u001b[43massociation_types\u001b[49m:\n\u001b[32m 2\u001b[39m list_association_types(association_type)\n", + "\u001b[31mNameError\u001b[39m: name 'association_types' is not defined" ] } ], @@ -169,50 +217,118 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-06-27 00:51:41.232\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n" + "\u001b[32m2025-06-27 09:06:54.592\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mGetting article text from PMCID: PMC11730665\u001b[0m\n", + "\u001b[32m2025-06-27 09:06:54.593\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", + "\u001b[32m2025-06-27 09:06:54.594\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m14\u001b[0m - \u001b[1mStep 1: Extracting variants from article\u001b[0m\n", + "\u001b[32m2025-06-27 09:06:54.594\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n", + "\u001b[32m2025-06-27 09:07:27.675\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m63\u001b[0m - \u001b[1mFound 10 variants\u001b[0m\n", + "\u001b[32m2025-06-27 09:07:27.679\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m16\u001b[0m - \u001b[1mExtracted 10 variants: ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs6923761', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n", + "\u001b[32m2025-06-27 09:07:27.680\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1mStep 2: Determining association types for variants\u001b[0m\n", + "\u001b[32m2025-06-27 09:07:27.681\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mget_association_types\u001b[0m:\u001b[36m107\u001b[0m - \u001b[1mDetermining association type for variants ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs6923761', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.934\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m25\u001b[0m - \u001b[1mStep 3: Categorizing variants by association type\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.936\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs2909451 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to sitagliptin treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.937\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs4664443 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to sitagliptin treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.938\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs3765467 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to sitagliptin treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.938\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs2285676 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to sitagliptin treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.939\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m121\u001b[0m - \u001b[34m\u001b[1mVariant rs6923761 has phenotype association: The variant is associated with clinical outcomes, specifically the glycemic response to sitagliptin treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.940\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs163184 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to sitagliptin treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.940\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs7754840 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to sitagliptin treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.941\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs756992 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to sitagliptin treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.941\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs1799853 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to gliclazide treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.942\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36m_categorize_variants\u001b[0m:\u001b[36m117\u001b[0m - \u001b[34m\u001b[1mVariant rs1057910 has drug association: The variant is associated with drug efficacy, indicating how it affects the response to gliclazide treatment.\u001b[0m\n", + "\u001b[32m2025-06-27 09:08:53.942\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m27\u001b[0m - \u001b[1mFinal categorization: 9 drug, 1 phenotype, 0 functional associations\u001b[0m\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-06-27 00:51:41.234\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.article_parser\u001b[0m:\u001b[36mremove_references_section\u001b[0m:\u001b[36m90\u001b[0m - \u001b[1mRemoved References section from article text\u001b[0m\n", - "\u001b[32m2025-06-27 00:51:41.235\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mStep 1: Extracting variants from article\u001b[0m\n", - "\u001b[32m2025-06-27 00:51:41.236\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msrc.utils\u001b[0m:\u001b[36mget_article_text\u001b[0m:\u001b[36m96\u001b[0m - \u001b[33m\u001b[1mBoth article_text and PMCID are provided. Using article_text.\u001b[0m\n", - "\u001b[32m2025-06-27 00:51:41.237\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mExtracting all variants\u001b[0m\n", - "\u001b[32m2025-06-27 00:52:00.050\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.all_variants\u001b[0m:\u001b[36mextract_all_variants\u001b[0m:\u001b[36m63\u001b[0m - \u001b[1mFound 10 variants\u001b[0m\n", - "\u001b[32m2025-06-27 00:52:00.051\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m51\u001b[0m - \u001b[1mExtracted 10 variants: ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs6923761', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n", - "\u001b[32m2025-06-27 00:52:00.051\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m62\u001b[0m - \u001b[1mStep 2: Determining association types for variants\u001b[0m\n", - "\u001b[32m2025-06-27 00:52:00.052\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msrc.utils\u001b[0m:\u001b[36mget_article_text\u001b[0m:\u001b[36m96\u001b[0m - \u001b[33m\u001b[1mBoth article_text and PMCID are provided. Using article_text.\u001b[0m\n", - "\u001b[32m2025-06-27 00:52:00.052\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.association_types\u001b[0m:\u001b[36mget_association_types\u001b[0m:\u001b[36m107\u001b[0m - \u001b[1mDetermining association type for variants ['rs2909451', 'rs4664443', 'rs3765467', 'rs2285676', 'rs6923761', 'rs163184', 'rs7754840', 'rs756992', 'rs1799853', 'rs1057910']\u001b[0m\n", - "\u001b[32m2025-06-27 00:53:18.632\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.components.variant_association_pipeline\u001b[0m:\u001b[36mprocess_article\u001b[0m:\u001b[36m74\u001b[0m - \u001b[1mStep 3: Categorizing variants by association type\u001b[0m\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'list' object has no attribute 'association_types'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcomponents\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvariant_association_pipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m run_variant_association_pipeline\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mrun_variant_association_pipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPMC11730665\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/components/variant_association_pipeline.py:153\u001b[39m, in \u001b[36mrun_variant_association_pipeline\u001b[39m\u001b[34m(article_text, pmcid, model, temperature)\u001b[39m\n\u001b[32m 140\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 141\u001b[39m \u001b[33;03mConvenience function to run the variant association pipeline.\u001b[39;00m\n\u001b[32m 142\u001b[39m \u001b[33;03m\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 150\u001b[39m \u001b[33;03m Dictionary with lists of variants for each association type\u001b[39;00m\n\u001b[32m 151\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 152\u001b[39m pipeline = VariantAssociationPipeline(model=model, temperature=temperature)\n\u001b[32m--> \u001b[39m\u001b[32m153\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpipeline\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprocess_article\u001b[49m\u001b[43m(\u001b[49m\u001b[43marticle_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43marticle_text\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpmcid\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/stanford/research/daneshjou/AutoGKB/src/components/variant_association_pipeline.py:75\u001b[39m, in \u001b[36mVariantAssociationPipeline.process_article\u001b[39m\u001b[34m(self, article_text, pmcid)\u001b[39m\n\u001b[32m 73\u001b[39m \u001b[38;5;66;03m# Step 3: Categorize variants by association type\u001b[39;00m\n\u001b[32m 74\u001b[39m logger.info(\u001b[33m\"\u001b[39m\u001b[33mStep 3: Categorizing variants by association type\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m75\u001b[39m result = \u001b[38;5;28mself\u001b[39m._categorize_variants(variants, \u001b[43massociation_types_result\u001b[49m\u001b[43m.\u001b[49m\u001b[43massociation_types\u001b[49m)\n\u001b[32m 77\u001b[39m logger.info(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFinal categorization: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(result[\u001b[33m'\u001b[39m\u001b[33mdrug_associations\u001b[39m\u001b[33m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m drug, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 78\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(result[\u001b[33m'\u001b[39m\u001b[33mphenotype_associations\u001b[39m\u001b[33m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m phenotype, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 79\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(result[\u001b[33m'\u001b[39m\u001b[33mfunctional_associations\u001b[39m\u001b[33m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m functional associations\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "\u001b[31mAttributeError\u001b[39m: 'list' object has no attribute 'association_types'" - ] + "data": { + "text/plain": [ + "{'drug_associations': [Variant(variant_id='rs2909451', gene='DPP4', allele='TT', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", + " Variant(variant_id='rs4664443', gene='DPP4', allele='GG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", + " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", + " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", + " Variant(variant_id='rs163184', gene='KCNQ1', allele='GG', evidence='KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group (P < .001), suggesting lower responsiveness to sitagliptin and better response to gliclazide.'),\n", + " Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs7754840 CG genotype showed a median improvement of 1.44 (IQR, 1.38–1.72) in the study group compared with 1.09 (IQR, 0.79–1.17) in the control group (P = .053).'),\n", + " Variant(variant_id='rs756992', gene='CDKAL1', allele='AG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs756992 AG genotype exhibited a median improvement of 1.43 (IQR, 1.28–1.52) in the study group compared with 1.10 (IQR, 0.87–1.18) in the control group (P = .081).'),\n", + " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT', evidence='CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).'),\n", + " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (P = .464).')],\n", + " 'phenotype_associations': [Variant(variant_id='rs6923761', gene='GLP1R', allele='AA', evidence='Regarding GLP1R gene polymorphisms, patients with the rs6923761 AA homozygous genotype in the study group had a median HbA1c improvement of 0.90 (IQR, 0.61–1.01), while the control group showed 1.41 (IQR, 1.12–1.45; P = .010), suggesting reduced glycemic response to sitagliptin.')],\n", + " 'functional_associations': []}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "from src.components.variant_association_pipeline import run_variant_association_pipeline\n", "\n", - "run_variant_association_pipeline(pmcid=\"PMC11730665\")" + "associations = run_variant_association_pipeline(pmcid=\"PMC11730665\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from src.variants import Variant" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "x = {'drug_associations': [Variant(variant_id='rs2909451', gene='DPP4', allele='TT', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", + " Variant(variant_id='rs4664443', gene='DPP4', allele='GG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", + " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", + " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", + " Variant(variant_id='rs163184', gene='KCNQ1', allele='GG', evidence='KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group (P < .001), suggesting lower responsiveness to sitagliptin and better response to gliclazide.'),\n", + " Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs7754840 CG genotype showed a median improvement of 1.44 (IQR, 1.38–1.72) in the study group compared with 1.09 (IQR, 0.79–1.17) in the control group (P = .053).'),\n", + " Variant(variant_id='rs756992', gene='CDKAL1', allele='AG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs756992 AG genotype exhibited a median improvement of 1.43 (IQR, 1.28–1.52) in the study group compared with 1.10 (IQR, 0.87–1.18) in the control group (P = .081).'),\n", + " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT', evidence='CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).'),\n", + " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (P = .464).')],\n", + " 'phenotype_associations': [Variant(variant_id='rs6923761', gene='GLP1R', allele='AA', evidence='Regarding GLP1R gene polymorphisms, patients with the rs6923761 AA homozygous genotype in the study group had a median HbA1c improvement of 0.90 (IQR, 0.61–1.01), while the control group showed 1.41 (IQR, 1.12–1.45; P = .010), suggesting reduced glycemic response to sitagliptin.')],\n", + " 'functional_associations': []}" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'drug_associations': [Variant(variant_id='rs2909451', gene='DPP4', allele='TT', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", + " Variant(variant_id='rs4664443', gene='DPP4', allele='GG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: dipeptidyl peptidase-4 rs2909451 TT and rs4664443 GG genotypes showed lower efficacy with sitagliptin.'),\n", + " Variant(variant_id='rs3765467', gene='GLP1R', allele='AG', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", + " Variant(variant_id='rs2285676', gene='KCNJ11', allele='CC', evidence='Genetic analysis showed specific single-nucleotide polymorphisms affected drug efficacy: GLP1R rs3765467 AG and KCNJ11 rs2285676 CC genotypes responded better to sitagliptin.'),\n", + " Variant(variant_id='rs163184', gene='KCNQ1', allele='GG', evidence='KCNQ1 gene polymorphisms also significantly affected treatment outcomes. Patients with the rs163184 GG allele in the study group had a median HbA1c improvement of 0.81 (IQR, 0.62–0.92) compared with 1.16 (IQR, 0.91–1.32) in the control group (P < .001), suggesting lower responsiveness to sitagliptin and better response to gliclazide.'),\n", + " Variant(variant_id='rs7754840', gene='CDKAL1', allele='CG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs7754840 CG genotype showed a median improvement of 1.44 (IQR, 1.38–1.72) in the study group compared with 1.09 (IQR, 0.79–1.17) in the control group (P = .053).'),\n", + " Variant(variant_id='rs756992', gene='CDKAL1', allele='AG', evidence='CDKAL1 gene variants, specifically rs7754840 CG and rs756992 AG, were associated with more significant HbA1c reductions in the study group. Patients with the rs756992 AG genotype exhibited a median improvement of 1.43 (IQR, 1.28–1.52) in the study group compared with 1.10 (IQR, 0.87–1.18) in the control group (P = .081).'),\n", + " Variant(variant_id='rs1799853', gene='CYP2C9', allele='TT', evidence='CYP2C9 gene polymorphisms also significantly influenced treatment efficacy. Patients with the rs1799853 TT genotype in the study group had a median HbA1c improvement of 0.70 (IQR, 0.69–0.72), while the control group showed 1.07 (IQR, 0.82–1.42; P < .001).'),\n", + " Variant(variant_id='rs1057910', gene='CYP2C9', allele='GG', evidence='For the rs1057910 GG genotype, the study group exhibited a median improvement of 0.93 (IQR, 0.66–1.21) compared with 1.20 (IQR, 0.89–1.30) in the control group (P = .464).')],\n", + " 'phenotype_associations': [Variant(variant_id='rs6923761', gene='GLP1R', allele='AA', evidence='Regarding GLP1R gene polymorphisms, patients with the rs6923761 AA homozygous genotype in the study group had a median HbA1c improvement of 0.90 (IQR, 0.61–1.01), while the control group showed 1.41 (IQR, 1.12–1.45; P = .010), suggesting reduced glycemic response to sitagliptin.')],\n", + " 'functional_associations': []}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" ] }, { diff --git a/src/components/all_variants.py b/src/components/all_variants.py index 42941a4..e076cfb 100644 --- a/src/components/all_variants.py +++ b/src/components/all_variants.py @@ -5,6 +5,7 @@ from loguru import logger import json from typing import List +from src.config import DEBUG VARIANT_LIST_KEY_QUESTION = """From this article, note down ALL discussed variants/haplotypes (ex. rs113993960, CYP1A1*1, etc.). Include information on the gene group and allele (if present). Make sure they variant has a studied association (likely discussed in the methodology or results section), not simply mentioned as background information. @@ -25,7 +26,6 @@ def extract_all_variants( pmcid: str = None, model: str = "gpt-4o", temperature: float = 0.1, - debug: bool = False, ) -> List[Variant]: """Extract a list of variants from an article. Args: @@ -37,7 +37,7 @@ def extract_all_variants( """ article_text = get_article_text(pmcid=pmcid, article_text=article_text) - if debug: + if DEBUG: logger.debug(f"Model: {model}, Temperature: {temperature}") logger.debug(f"PMCID: {pmcid}") @@ -52,10 +52,10 @@ def extract_all_variants( hydrated_prompt = prompt_generator.hydrate_prompt() logger.info(f"Extracting all variants") output = model.prompted_generate(hydrated_prompt) - if debug: + if DEBUG: logger.debug(f"Raw LLM output: {output}") parsed_output = json.loads(output) - if debug: + if DEBUG: logger.debug(f"Parsed output: {parsed_output}") variant_list = [ Variant(**variant_data) for variant_data in parsed_output["variant_list"] diff --git a/src/components/association_types.py b/src/components/association_types.py index 7d946f1..99fb0e9 100644 --- a/src/components/association_types.py +++ b/src/components/association_types.py @@ -9,7 +9,7 @@ from pydantic import BaseModel from src.utils import get_article_text from loguru import logger - +from src.config import DEBUG class AssociationType(BaseModel): """ @@ -144,12 +144,12 @@ def get_association_types( def list_association_types( - association_type: AssociationType, debug: bool = False + association_type: AssociationType ) -> List[str]: association_types = [] if association_type.drug_association: association_types.append("Drug") - if debug: + if DEBUG: logger.debug(f"Drug Association: {association_type.drug_association}") logger.debug( f"Drug Association Explanation: {association_type.drug_association_explanation}" @@ -159,7 +159,7 @@ def list_association_types( ) if association_type.phenotype_association: association_types.append("Phenotype") - if debug: + if DEBUG: logger.debug( f"Phenotype Association: {association_type.phenotype_association}" ) @@ -171,7 +171,7 @@ def list_association_types( ) if association_type.functional_association: association_types.append("Functional") - if debug: + if DEBUG: logger.debug( f"Functional Association: {association_type.functional_association}" ) diff --git a/src/components/variant_association_pipeline.py b/src/components/variant_association_pipeline.py index 4ae80d8..db1ae9d 100644 --- a/src/components/variant_association_pipeline.py +++ b/src/components/variant_association_pipeline.py @@ -19,6 +19,7 @@ from src.utils import get_article_text from src.variants import Variant +from src.config import DEBUG class VariantAssociationPipeline: """Pipeline to extract variants and determine their association types from an article.""" @@ -72,7 +73,7 @@ def process_article( # Step 3: Categorize variants by association type logger.info("Step 3: Categorizing variants by association type") - result = self._categorize_variants(variants, association_types_result.association_types) + result = self._categorize_variants(variants, association_types_result) logger.info(f"Final categorization: {len(result['drug_associations'])} drug, " f"{len(result['phenotype_associations'])} phenotype, " @@ -114,15 +115,18 @@ def _categorize_variants( # Categorize based on association types if association.drug_association: drug_associations.append(variant) - logger.debug(f"Variant {variant.variant_id} has drug association: {association.drug_association_explanation}") + if DEBUG: + logger.debug(f"Variant {variant.variant_id} has drug association: {association.drug_association_explanation}") if association.phenotype_association: phenotype_associations.append(variant) - logger.debug(f"Variant {variant.variant_id} has phenotype association: {association.phenotype_association_explanation}") + if DEBUG: + logger.debug(f"Variant {variant.variant_id} has phenotype association: {association.phenotype_association_explanation}") if association.functional_association: functional_associations.append(variant) - logger.debug(f"Variant {variant.variant_id} has functional association: {association.functional_association_explanation}") + if DEBUG: + logger.debug(f"Variant {variant.variant_id} has functional association: {association.functional_association_explanation}") return { "drug_associations": drug_associations, diff --git a/src/config.py b/src/config.py new file mode 100644 index 0000000..6f2cf44 --- /dev/null +++ b/src/config.py @@ -0,0 +1,55 @@ +""" +Configuration module for AutoGKB. + +This module handles debug mode configuration and logging setup. +""" + +from loguru import logger +from typing import NoReturn +import sys + +# Global debug flag +DEBUG: bool = False + +if DEBUG: + logger.debug("Debug mode is enabled") + + +def set_debug(debug: bool) -> NoReturn: + """ + Set the debug mode globally. + + Args: + debug: Boolean flag to enable/disable debug mode + """ + global DEBUG + DEBUG = debug + + if DEBUG: + logger.debug("Debug mode enabled") + else: + logger.debug("Debug mode disabled") + + +def save_logs(save: bool = False) -> NoReturn: + """ + Configure logging to save logs to a file. + + Args: + save: Boolean flag to enable/disable log file saving (default: False) + """ + if save: + # Remove default console handler and add file handler + logger.remove() + logger.add( + "autogkb.log", + rotation="10 MB", + retention="7 days", + level="DEBUG" + ) + logger.info("Logs will be saved to autogkb.log") + else: + # Remove all handlers and add back console handler + logger.remove() + logger.add(sys.stderr, level="INFO") + logger.info("Logs will be output to console only") \ No newline at end of file From 0543dc12ad400f0092cad09305ba7cfd613f1293 Mon Sep 17 00:00:00 2001 From: Shlok Natarajan Date: Sat, 28 Jun 2025 09:39:01 -0700 Subject: [PATCH 28/28] chore: black formatting --- src/components/association_types.py | 5 +- .../variant_association_pipeline.py | 95 ++++++++++--------- src/config.py | 15 +-- src/variants.py | 3 +- 4 files changed, 60 insertions(+), 58 deletions(-) diff --git a/src/components/association_types.py b/src/components/association_types.py index 99fb0e9..ead9d60 100644 --- a/src/components/association_types.py +++ b/src/components/association_types.py @@ -11,6 +11,7 @@ from loguru import logger from src.config import DEBUG + class AssociationType(BaseModel): """ Variant Association Type @@ -143,9 +144,7 @@ def get_association_types( return None -def list_association_types( - association_type: AssociationType -) -> List[str]: +def list_association_types(association_type: AssociationType) -> List[str]: association_types = [] if association_type.drug_association: association_types.append("Drug") diff --git a/src/components/variant_association_pipeline.py b/src/components/variant_association_pipeline.py index db1ae9d..36882f5 100644 --- a/src/components/variant_association_pipeline.py +++ b/src/components/variant_association_pipeline.py @@ -21,139 +21,146 @@ from src.config import DEBUG + class VariantAssociationPipeline: """Pipeline to extract variants and determine their association types from an article.""" - + def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.1): self.model = model self.temperature = temperature - + def process_article( - self, - article_text: str = None, - pmcid: str = None + self, article_text: str = None, pmcid: str = None ) -> Dict[str, List[Variant]]: """ Process an article to extract variants and determine their association types. - + Args: article_text: The text of the article pmcid: The PMCID of the article - + Returns: Dictionary with lists of variants for each association type """ # Get article text article_text = get_article_text(pmcid=pmcid, article_text=article_text) - + # Step 1: Extract all variants logger.info("Step 1: Extracting variants from article") variants = extract_all_variants(article_text, pmcid) - logger.info(f"Extracted {len(variants)} variants: {[v.variant_id for v in variants]}") - + logger.info( + f"Extracted {len(variants)} variants: {[v.variant_id for v in variants]}" + ) + if not variants: logger.warning("No variants found in article") return { "drug_associations": [], "phenotype_associations": [], - "functional_associations": [] + "functional_associations": [], } - + # Step 2: Determine association types for all variants logger.info("Step 2: Determining association types for variants") association_types_result = get_association_types(variants, article_text, pmcid) - + if association_types_result is None: logger.error("Failed to determine association types") return { "drug_associations": [], "phenotype_associations": [], - "functional_associations": [] + "functional_associations": [], } - + # Step 3: Categorize variants by association type logger.info("Step 3: Categorizing variants by association type") result = self._categorize_variants(variants, association_types_result) - - logger.info(f"Final categorization: {len(result['drug_associations'])} drug, " - f"{len(result['phenotype_associations'])} phenotype, " - f"{len(result['functional_associations'])} functional associations") - + + logger.info( + f"Final categorization: {len(result['drug_associations'])} drug, " + f"{len(result['phenotype_associations'])} phenotype, " + f"{len(result['functional_associations'])} functional associations" + ) + return result - + def _categorize_variants( - self, - variants: List[Variant], - association_types: List[AssociationType] + self, variants: List[Variant], association_types: List[AssociationType] ) -> Dict[str, List[Variant]]: """ Categorize variants based on their association types. - + Args: variants: List of variants association_types: List of association type results - + Returns: Dictionary with variants categorized by association type """ drug_associations = [] phenotype_associations = [] functional_associations = [] - + # Create a mapping from variant_id to association_type for easy lookup variant_to_association = { assoc.variant.variant_id: assoc for assoc in association_types } - + for variant in variants: association = variant_to_association.get(variant.variant_id) - + if association is None: - logger.warning(f"No association type found for variant {variant.variant_id}") + logger.warning( + f"No association type found for variant {variant.variant_id}" + ) continue - + # Categorize based on association types if association.drug_association: drug_associations.append(variant) if DEBUG: - logger.debug(f"Variant {variant.variant_id} has drug association: {association.drug_association_explanation}") - + logger.debug( + f"Variant {variant.variant_id} has drug association: {association.drug_association_explanation}" + ) + if association.phenotype_association: phenotype_associations.append(variant) if DEBUG: - logger.debug(f"Variant {variant.variant_id} has phenotype association: {association.phenotype_association_explanation}") - + logger.debug( + f"Variant {variant.variant_id} has phenotype association: {association.phenotype_association_explanation}" + ) + if association.functional_association: functional_associations.append(variant) if DEBUG: - logger.debug(f"Variant {variant.variant_id} has functional association: {association.functional_association_explanation}") - + logger.debug( + f"Variant {variant.variant_id} has functional association: {association.functional_association_explanation}" + ) + return { "drug_associations": drug_associations, "phenotype_associations": phenotype_associations, - "functional_associations": functional_associations + "functional_associations": functional_associations, } def run_variant_association_pipeline( - article_text: str = None, + article_text: str = None, pmcid: str = None, model: str = "gpt-4o-mini", - temperature: float = 0.1 + temperature: float = 0.1, ) -> Dict[str, List[Variant]]: """ Convenience function to run the variant association pipeline. - + Args: article_text: The text of the article pmcid: The PMCID of the article model: The LLM model to use temperature: The temperature for LLM generation - + Returns: Dictionary with lists of variants for each association type """ pipeline = VariantAssociationPipeline(model=model, temperature=temperature) return pipeline.process_article(article_text=article_text, pmcid=pmcid) - - \ No newline at end of file diff --git a/src/config.py b/src/config.py index 6f2cf44..85bdcea 100644 --- a/src/config.py +++ b/src/config.py @@ -18,13 +18,13 @@ def set_debug(debug: bool) -> NoReturn: """ Set the debug mode globally. - + Args: debug: Boolean flag to enable/disable debug mode """ global DEBUG DEBUG = debug - + if DEBUG: logger.debug("Debug mode enabled") else: @@ -34,22 +34,17 @@ def set_debug(debug: bool) -> NoReturn: def save_logs(save: bool = False) -> NoReturn: """ Configure logging to save logs to a file. - + Args: save: Boolean flag to enable/disable log file saving (default: False) """ if save: # Remove default console handler and add file handler logger.remove() - logger.add( - "autogkb.log", - rotation="10 MB", - retention="7 days", - level="DEBUG" - ) + logger.add("autogkb.log", rotation="10 MB", retention="7 days", level="DEBUG") logger.info("Logs will be saved to autogkb.log") else: # Remove all handlers and add back console handler logger.remove() logger.add(sys.stderr, level="INFO") - logger.info("Logs will be output to console only") \ No newline at end of file + logger.info("Logs will be output to console only") diff --git a/src/variants.py b/src/variants.py index 9610883..e165f44 100644 --- a/src/variants.py +++ b/src/variants.py @@ -1,6 +1,7 @@ from pydantic import BaseModel from typing import List + class Variant(BaseModel): """Variant.""" @@ -13,4 +14,4 @@ class Variant(BaseModel): class VariantList(BaseModel): """List of variants.""" - variant_list: List[Variant] \ No newline at end of file + variant_list: List[Variant]