Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Seonghun Kim, Seockhun Bae, Yinhua Piao and Kyuri Jo
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Seonghun Kim: Department of Computer Engineering, Chungbuk National University, Cheongju 28644, Korea
Seockhun Bae: Department of Computer Engineering, Chungbuk National University, Cheongju 28644, Korea
Yinhua Piao: Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
Kyuri Jo: Department of Computer Engineering, Chungbuk National University, Cheongju 28644, Korea

Mathematics, 2021, vol. 9, issue 7, 1-17

Abstract: Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

Keywords: neural network; graph convolutional network; spectral graph theory; drug response; bioinformatics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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