MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

Tongxin Wang, Wei Shao, Zhi Huang, Haixu Tang, Jie Zhang, Zhengming Ding () and Kun Huang ()
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Tongxin Wang: Indiana University Bloomington
Wei Shao: Indiana University School of Medicine
Zhi Huang: Indiana University School of Medicine
Haixu Tang: Indiana University Bloomington
Jie Zhang: Indiana University School of Medicine
Zhengming Ding: Tulane University
Kun Huang: Indiana University School of Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.

Date: 2021
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DOI: 10.1038/s41467-021-23774-w

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