Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity
W. Ba-alawi, S. K. Nair, B. Li, A. Mammoliti, P. Smirnov, A. S. Mer, L. Penn, B. Haibe-Kains
The paper develops an interpretable machine learning pipeline to predict drug sensitivity in cancer cells using bimodal gene expression patterns from pharmacogenomic data.
bioRxiv · Sep 8, 2020 · PreprintCancer Biology