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Cited by 51Bioinformatics and Genomic NetworksCancer Genomics and DiagnosticsGene expression and cancer classification

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology

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Wang, Feng-ao, Zhuang, Zhenfeng, Gao, Feng, He, Ruikun, Zhang, Shaoting, Wang, Liansheng, Liu, Junwei, Li, Yixue·2024·Genome Biology
DOI 10.1186/s13059-024-03293-9PMC PMC11157742
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Abstract

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

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  • The development of incomplete modal learning enables more flexible and efficient representation learning in multimodal datasets.

    “Additionally, the development of incomplete modal learning enables more flexible and efficient representation learning in multimodal datasets [54,55].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [55]

  • The authors developed the TMO-Net model for incomplete multi-omics data learning of cancer data and adapted it into multiple downstream tasks.

    “We also developed the TMO-Net model for incomplete multi-omics data learning of cancer data and adapted it into multiple downstream tasks [55].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [55]

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Cited by

Sorted by citations

Authors
Wang, F.-A., Zhuang, Z., Gao, F., He, R., Zhang, S., Wang, L., Liu, J., & Li, Y.
Affiliations
  • University of Chinese Academy of Sciences (CN)
  • Xiamen University (CN)
  • Sun Yat-sen University (CN)
  • Shanghai Artificial Intelligence Laboratory (CN)
  • Guangzhou Laboratory (CN)
  • Shanghai Jiao Tong University (CN)
Corresponding author
  • Wang, Liansheng
  • Liu, Junwei
  • Li, Yixue
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2024-06-06
Volume
25
Issue
1
Pages
149-149
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-024-03293-9
PMC
PMC11157742
OpenAlex
W4399390205
Cited by
51
References
10
Citations by year
2026: 15 · 2025: 32 · 2024: 4
FWCI
7.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • CAS Research Fund (XDB38050200)
  • Self-supporting Program of Guangzhou Laboratory (SRPG22001, SRPG22007)
  • Key Technologies Research and Development Program (2022YFF1202101)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
multi-omics integration, pan-cancer, multi-task learning, pre-training, representation learning, explainable learning, interpretable learning, clinical outcomes, foundation models, oncology
MeSH terms
Machine Learning, Multiomics, Humans, Medical Oncology, Neoplasms, Genomics

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