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.

Model Type

Regression

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