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

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

Drug Response Estimation from single-cell Expression Profiles

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.

Deep Learning based Drug Response Predication with public Omics datasets

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

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

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

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

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