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

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

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

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

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

CrossTx: Cross-cell line Transcriptomic Signature Predictions

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

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

An extended Python package for topological regression for quantitative structure-activity relationship modeling

Pan Cancer Pan Treatment Github Repository