DrEval is a toolkit that ensures drug response prediction evaluations are statistically sound, biologically meaningful, and reproducible.
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Updated
Sep 21, 2026 - Python
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)
Knowledge graph-enhanced prediction of biomedical molecular relationships with MolNexus
Framework to build, evaluate, select, and compare ML classification and regression models using high-dimensional biological data and other covariates
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
Drug response classification using Support Vector Machine (SVM) with EDA, feature engineering, hyperparameter tuning, and kernel comparison for healthcare analytics.
💊 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.
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
💊 Predict drug responses using multi-omics data with this advanced platform, enhancing precision medicine in oncology through effective computational analysis.
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.
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