Solving Biological Challenges, Seeking Causes in Life Sciences

SolvingLab is a bioinformatics research organization dedicated to developing computational methodologies and software tools for multi-omics data analysis. Our research focuses on integrating genomics, transcriptomics, and proteomics data to address fundamental questions in cancer biology, immunology, and precision medicine.

Cancer Genomics & Multi-Omics Integration
Development of computational frameworks for analyzing large-scale cancer genomic datasets, including TCGA and CPTAC. Our tools enable comprehensive characterization of tumor molecular profiles and identification of clinically relevant biomarkers.

Computational Immunology
Algorithmic approaches for tumor microenvironment deconvolution, immune cell infiltration quantification, and immune checkpoint therapy response prediction.

Systems Biology & Network Analysis
Integration of molecular interaction networks with functional genomics data to understand biological systems and disease mechanisms.

AI-Driven Biological Discovery
Application of large language models and machine learning approaches to automate and enhance biological data interpretation and hypothesis generation.

OmixBench ·
A systematic evaluation framework for assessing large language model performance in multi-omics analysis tasks.

SLTCGA
Comprehensive R package for multi-omics data retrieval, processing, and analysis from The Cancer Genome Atlas (TCGA).

SLCPTAC
Multi-omics analysis toolkit specifically designed for Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets, enabling integrated proteogenomic analysis.

BioEnricher
Modular pipeline for functional enrichment analysis, pathway visualization, and biological interpretation of high-throughput sequencing data.

GenePerturbR
Statistical framework for analyzing gene perturbation effects using RNA-seq databases and CRISPR screening data.

astat
Streamlined statistical analysis toolkit designed for biological and clinical data analysis with an emphasis on reproducibility.

Our computational pipelines are primarily implemented in R and Python, leveraging established bioinformatics frameworks including Bioconductor, Biopython, and scikit-learn. We prioritize:

• Reproducibility: Well-documented code with comprehensive vignettes
• Scalability: Efficient algorithms for large-scale omics data
• Interoperability: Standard data formats and API design
• Usability: Clear interfaces and extensive documentation

We welcome collaboration with academic institutions, research hospitals, and biotechnology organizations. Our tools are designed to facilitate:

• Multi-institutional cancer genomics studies
• Biomarker discovery and validation projects
• Therapeutic target identification research
• Educational programs in computational biology

Contact: liuzaoqu@163.com

All software packages developed by SolvingLab are released as open-source projects under GPL-3.0 or MIT licenses. We believe in the principles of open science and reproducible research, making our tools freely available to the global research community.

Contributions are welcome through GitHub pull requests. Please refer to individual repository guidelines for contribution standards and code review processes.

# Standard contribution workflow
git clone https://github.com/SolvingLab/[repository].git
git checkout -b feature/your-contribution
# Make changes and commit
git push origin feature/your-contribution
# Submit pull request

• GitHub Organization: https://github.com/SolvingLab
• Documentation: Available in individual repository wikis
• Issue Tracking: GitHub Issues for bug reports and feature requests