Expanding the Landscape of Drug Targets for 112 Chronic Diseases Using a Machine Learning-Assisted Genetic Priority Score

We developed a Machine Learning-assisted Genetic Priority Score (ML-GPS) that incorporates genetic associations with predicted disease phenotypes to enhance target discovery. Dependencies: - Python 3.11.6 - scikit-learn 1.4.1 - LightGBM 4.0.0 - scipy 1.12.0 - statsmodels 0.14.1 Jupyter notebooks: 1.

Phecode diagnosis prediction models - code to train phecode diagnosis prediction models among UK Biobank participants. 2. ML-GPS models - code to train ML-GPS in Open Targets and externally test it in SIDER. Cleaned Open Targets and SIDER datasets are in the "Datasets" folder.