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CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated

Wei Wang1, Xi Yang1, Chengkun Wu2,3

  • 1College of Computer, National University of Defense Technology, Changsha, 410073, China.

BMC Bioinformatics
|November 27, 2020
PubMed
Summary

We developed CGINet, a graph convolutional network method to predict chemical-gene interactions. Our approach effectively identifies potential associations by integrating chemicals, genes, and pathways in a multi-relational graph, improving drug discovery and repositioning.

Keywords:
Chemical-gene interactionDrug discoveryGraph convolutional networkIntegrated multi-relational graph

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Network Science

Background:

  • Understanding chemical-gene interactions is crucial for drug discovery and repositioning.
  • Biological network approaches show promise in predicting these interactions.

Purpose of the Study:

  • To present CGINet, a novel graph convolutional network method for identifying chemical-gene interactions.
  • To explore different node embedding learning strategies for improved prediction accuracy.

Main Methods:

  • CGINet utilizes an integrated multi-relational graph with chemicals, genes, and pathways.
  • It employs two node embedding perspectives: whole graph and subgraph views.
  • The method uses an end-to-end training approach for encoder and decoder with known interactions.

Main Results:

  • CGINet models demonstrate competitive performance in identifying chemical-gene interactions.
  • The subgraph perspective and latent link reconstruction enhance prediction accuracy.
  • Three CGINet implementations (CGINet-1/2/3) were evaluated against baseline methods.

Conclusions:

  • CGINet effectively predicts potential chemical-gene associations and their interaction types.
  • The proposed methods, particularly the subgraph view and latent links, improve informative node embeddings.
  • This approach offers a promising tool for advancing drug development and target identification.