drug-response-prediction

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.

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

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

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

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

Deep Learning based Drug Response Predication with public Omics datasets

Tensorflow implementation of PaccMann (drug sensitivity prediction)

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

Drug response classification using Support Vector Machine (SVM) with EDA, feature engineering, hyperparameter tuning, and kernel comparison for healthcare analytics.

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

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

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

💊 Advanced Drug Response Prediction & Multi-Omics Platform Interactive computational biology dashboard with ML integration, synthetic CCLE/GDSC data, dose-response modeling, and biomarker discovery. Features 6-tab Streamlit interface, Random Forest predictions, and publication-quality visualizations.

💊 Predict drug responses using multi-omics data with this advanced platform, enhancing precision medicine in oncology through effective computational analysis.

CrossTx: Cross-cell line Transcriptomic Signature Predictions

Project develops a mechanism-aware prediction pipeline using CTRPv2 drug response and cancer cell-line omics. It compares single-omics and integrated strategies, evaluates AAC prediction, and uses SHAP and pathway analysis to identify molecular drivers across drug MOA classes.