Welcome to my Biostatistics repository!
This repository is dedicated to storing and organizing code for projects related to biostatistics, with particular emphasis on biomedical and statistical genetics research.
This repository contains codebases, analyses, and scripts developed for a range of biostatistical applications, including but not limited to:
- Statistical modeling in biomedical research
- Computational methods in genetics and genomics
- Machine learning applications in biostatistics
- Exploratory data analysis and data visualization
Projects here are often motivated by real-world biomedical questions and are designed to contribute to advancements in biostatistical methodology and practice.
The repository includes:
analysis/— Scripts for data analysis and modelingdata/— Sample datasets (where applicable or simulated)
Key tools and languages used in this repository:
- R (primary language for statistical computing)
- Python (for machine learning and data manipulation)
- Libraries/Packages:
caret,xgboost,ggplot2,dplyr(R)numpy,pandas,scikit-learn(Python)
Some examples of projects included (or to be added):
- Gene expression prediction models using XGBoost
- High-dimensional factor analysis in genomics
- Biostatistical methods for analyzing clinical trial data
Important:
Some of the datasets used in this repository are not publicly available due to data-sharing restrictions or privacy considerations.
Wherever possible, simulated or example datasets have been used for demonstration.
Access to real datasets may require appropriate permissions or licensing from the original data providers.
This project is licensed under the MIT License.
Contributions are welcome! Feel free to open an issue or submit a pull request if you have improvements, ideas, or additional analyses to share.
For any questions, discussions, or collaborations, please feel free to reach out.
Would you also like a second version that sounds even slightly more formal (in case you’re thinking of publishing this with your paper)?
I can also add a "Citation" section if the code will support a published manuscript. 📄