DualGCN
A dual graph convolutional network model to predict cancer drug response
Model Architecture
DualGCN consists of two Graph Convolutional Networks (GCN) branches, one for protein-protein interaction information (which includes Copy Number Variation and Gene Expression), and one for the chemical structure of the applied drug. Each branch has built-in dropout and batch normalization, where the dropout rate is one of the considered hyperparameters. At the end of the branches, the obtained features are concatenated and fed into a fully connected network (FCN) with three hidden layers, aiming to do the regression analysis between the output of the two branches and the drug response values.
Feature Representation
Cancer features:
Copy Number Variation
Gene Expression
Drug features:
SMILES: converted into a graph using RDKit and DeepChem
Other features:
Protein-Protein Interaction
URLs
References
1. T. Ma, et. al. “DualGCN: a dual graph convolutional network model to predict cancer drug response”, BMC Bioinformatics, 2022