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

Tongxin WangDepartment of Computer Science, Indiana University Bloomington, Bloomington, IN, USAWei ShaoIndiana University – Purdue University IndianapolisZhi HuangSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USAHaixu TangDepartment of Computer Science, Indiana University Bloomington, Bloomington, IN, USAJie ZhangDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USAZhengming DingDepartment of Computer Science, Tulane University, New Orleans, LA, USA. [email protected]Kun HuangDepartment of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA

2021en

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ABI

Annotatsiya

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.

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DOI: 10.1038/s41467-021-23774-w

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