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Cited by 30Bioinformatics and Genomic NetworksGene expression and cancer classificationHealth, Environment, Cognitive Aging

HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification

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Wang, Haohua, Lin, Kai, Zhang, Qiang, Shi, Jinlong, Song, Xinyu, Wu, Jue, Zhao, Chenghui, He, Kunlun·2024·Bioinformatics
DOI 10.1093/bioinformatics/btae159PMC PMC11212491
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Abstract

Motivation: The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. Results: This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer’s disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. Availability and implementation: HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO

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  • PathTIGR consistently outperformed seven state-of-the-art deep learning frameworks (BPformer, DeepCC, HyperTMO, IRnet, MOGAT, MOGDx, and MOGONET) across all evaluated metrics on multiple immunotherapy datasets.

    “Despite their sophisticated computational frameworks, independent evaluation across multiple immunotherapy datasets revealed consistent underperformance relative to PathTIGR across all evaluated metrics (Fig. 5, A and B, and fig. S3).”

    From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [45]

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Cited by

Sorted by citations

Authors
Wang, H., Lin, K., Zhang, Q., Shi, J., Song, X., Wu, J., Zhao, C., & He, K.
Affiliations
  • Dalian University of Technology (CN)
  • Chinese PLA General Hospital (CN)
Corresponding author
Lin, Kai
ORCID
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2024-03-26
Volume
40
Issue
4
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btae159
PMC
PMC11212491
OpenAlex
W4393185961
Cited by
30
References
5
Citations by year
2026: 15 · 2025: 12 · 2024: 3
FWCI
4.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Fundamental Research Funds for the Central Universities (DUT21TD107)
  • LiaoNing Revitalization Talents Program (XLYC2008017)
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
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
Keywords
multi-omics integration, hypergraph convolutional network, patient classification, Dempster-Shafer theory, breast cancer subtyping, Alzheimer's disease classification, diagnosis
MeSH terms
Machine Learning, Multiomics, Alzheimer Disease, Breast, Breast Neoplasms, Female, Humans

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