<p class="jove_content">This protocol presents a machine learning pipeline using XGBoost and SHAP to predict drug sensitivity in breast cancer. The workflow includes data preprocessing, hybrid modeling, SHAP-based interpretation, synergy scoring, and PCA clustering to identify potent drugs and understand key biological factors influencing therapeutic response.</p>

data-driven-drug-discovery-optimization-for-breast-cancer-using

Data-Driven Drug Discovery Optimization for Breast Cancer Using Interpretable Machine Learning Models

Breast cancer remains one of the most prevalent malignancies worldwide, posing significant therapeutic challenges due to tumor heterogeneity and drug ...

Institution - Gurukula Kangri University

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