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Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction

Authors :
Kunjie Fan
Shan Tang
Birkan Gökbağ
Lijun Cheng
Lang Li
Source :
Frontiers in Genetics, Vol 13 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model’s successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.

Details

Language :
English
ISSN :
16648021
Volume :
13
Database :
Directory of Open Access Journals
Journal :
Frontiers in Genetics
Publication Type :
Academic Journal
Accession number :
edsdoj.555522724c9c4f8eb54c7303c3ce25b9
Document Type :
article
Full Text :
https://doi.org/10.3389/fgene.2022.1103092