Interpretable ML pipeline (Random Forest / XGBoost / SVM with SHAP) that predicts Poor vs. Intermediate CYP2C9 metabolizer phenotype for diplotypes of indeterminate function, and prioritizes high-impact variants for MD simulation and clinical review.
machine-learning bioinformatics random-forest xgboost pharmacogenomics precision-medicine shap drug-metabolism variant-classification cyp2c9
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Updated
May 16, 2026 - Python