PathDSP
Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores
Model Architecture
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.
Feature Representation
Cancer features:
Gene Expression: converted into pathway-level activity using ssGSEA algorithm
CNV: converted into pathway-level activity using NetPEA algorithm
Mutation: converted into pathway-level activity using NetPEA algorithm
Drug features:
SMILES: converted into morgan fingerprints
Drug target: converted into pathway-level activity using NetPEA algorithm
URLs
References
1. Y. Tang and A. Gottlieb. “Explainable drug sensitivity prediction through cancer pathway enrichment”, Scientific Reports, 2021.