wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning
Open accessAbstract
Background Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping. Methods We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision. Results wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes. Conclusions The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .
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MKL is commonly used in disease diagnosis and classification tasks within multi-omics studies.
“In contrast, MKL, a supervised machine learning approach, is commonly used in disease diagnosis and classification tasks within multi-omics studies [47,48].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [47]
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- Authors
- Cao, H., Jia, C., Li, Z., Yang, H., Fang, R., Zhang, Y., & Cui, Y.
- Affiliations
- Shanxi Medical University (CN)
- Hebei Medical University (CN)
- Michigan State University (US)
- Corresponding author
- Cui, Yuehua
- Venue
- British Journal of Cancer
- Published
- 2024-01-26
- Volume
- 130
- Issue
- 6
- Pages
- 1001-1012
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- PMC
- PMC10951206
- OpenAlex
- W4391259201
- Cited by
- 12
- References
- 66
- Citations by year
- 2026: 9 · 2025: 3
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- British Journal of Cancer (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Michigan State University
- National Natural Science Foundation of China (71403156, 81872717)
- China Scholarship Council (201908140151)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- cancer subtyping, multi-omics integration, multiple kernel learning, weight functions, kernel parameter selection, p-value combination, tumor heterogeneity, precision oncology, TCGA cancer datasets
- MeSH terms
- Multiomics, Humans, Neoplasms, Computational Biology
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