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This is a community model for drug response prediction curated as part of the IMPROVE project.
Enables prediction of drug response with drug and cancer features.
Pan-drug, pan-cancer, single drug response prediction model.
PathDSP first converts the cancer and drug features into pathway-level enrichment scores using a network-based approach, then feeds those into a fully connected neural network (FNN) with four hidden layers.
A data scientist can train the provided untrained model on their own data or use the trained model to classify the provided test samples. The provided scripts use the IMPROVE Benchmark Dataset.
The model-related files are located in Model and Data Clearinghouse (MoDaC), and the source code is on GitHub.
- Cancer features: Gene Expression, Mutation, Copy Number Variation
- Drug features: Drug Fingerprints, Drug Target
Predicted drug response values for a given sample and drug pair and metrics if ground truth is provided.