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

• Original GitHub

• IMPROVE GitHub

References

1. T. Ma, et. al. “DualGCN: a dual graph convolutional network model to predict cancer drug response”, BMC Bioinformatics, 2022