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Cited by 671Bioinformatics and Genomic NetworksGene expression and cancer classificationCancer-related molecular mechanisms research

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

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Wang, Tongxin, Shao, Wei, Huang, Zhi, Tang, Haixu, Zhang, Jie, Ding, Zhengming, Huang, Kun·2021·Nature Communications
DOI 10.1038/s41467-021-23774-wPMC PMC8187432

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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.

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  • Wang et al. proposed the GNN-based MOGONET method for biomedical sample classification and identified subgroup-specific biomarkers.

    “Wang et al. proposed a novel GNN-based method named multi-omics graph convolutional networks (MOGONET) for biomedical sample classification and identified the subgroup-specific biomarkers [113].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [113]

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Metadata

Authors
Wang, T., Shao, W., Huang, Z., Tang, H., Zhang, J., Ding, Z., & Huang, K.
Affiliations
  • Indiana University Bloomington (US)
  • Indiana University School of Medicine (US)
  • Purdue University West Lafayette (US)
  • Indiana University Indianapolis (US)
  • Tulane University (US)
  • Regenstrief Institute (US)
Corresponding author
  • Ding, Zhengming
  • Huang, Kun
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-06-08
Volume
12
Issue
1
Pages
3445-3445
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-021-23774-w
PMC
PMC8187432
OpenAlex
W3170986249
Cited by
671
References
10
Citations by year
2026: 178 · 2025: 173 · 2024: 150 · 2023: 98 · 2022: 55 · 2021: 15
FWCI
24.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion, cc-by)
  • Purdue University Indianapolis (Indiana University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (R01EB025018)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
Keywords
multi-omics integration, graph convolutional networks, patient classification, biomarker discovery, mRNA expression, DNA methylation, microRNA expression, biomedical classification
MeSH terms
Algorithms, Alzheimer Disease, Breast Neoplasms, Female, Humans, Biomarkers, Genomics, Databases, Genetic

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