Velodrome combines semi-supervised learning and out-of-distribution generalization (domain generalization) for drug response prediction and pharmacogenomics

CaDRReS-Sc is a framework for analyzing drug response heterogeneity based on single-cell RNA-seq data

DrEval is a toolkit that ensures drug response prediction evaluations are statistically sound, biologically meaningful, and reproducible.

Pipeline for testing drug response prediction models in a statistically and biologically sound way.

Drug Response Estimation from single-cell Expression Profiles

The Drug Response Prediction 2022 project in Computational Biology and Artificial Intelligence (COMBINE) Laboratory, McGill University.

DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling

Deep Learning based Drug Response Predication with public Omics datasets

Tensorflow implementation of PaccMann (drug sensitivity prediction)

Python implementation of TRANSACT, a tool to transfer non-linear predictors of drug response from model systems to tumors.

Framework to build, evaluate, select, and compare ML classification and regression models using high-dimensional biological data and other covariates

Implementation of Percolate, an exponential family JIVE statistical model for multi-view integration

Clinical and biological implications of differential expression of sex hormone-related genes in testicular cancer

DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling

The repo of the "Multi-omics alleviates the limitations of panel-sequencing for cancer drug response prediction" manuscript

CrossTx: Cross-cell line Transcriptomic Signature Predictions

Pan Cancer Pan Treatment Github Repository