Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

Gene-phecode pairs in the top 10% of ML-GPS scores can be accessed interactively at https://rstudio-connect.hpc.mssm.edu/mlgps/. This respository contains the following data: All ML-GPS and ML-GPS DOE predictions (Predictions for all gene-phecode pairs.zip) Summary statistics for all 112 phecodes (Summary statistics.zip) Cleaned genetics and drug datasets (Cleaned files to generate Open Targets and SIDER datasets.zip) Input datasets for ML-GPS and ML-GPS DOE training and prediction (Inputs for ML-GPS training and prediction.zip) Code to clean data and train models (Jupyter Notebooks.zip) Dependencies can be found within each Jupyter Notebook; these analyses were performed using Python 3.12.