KANTrust: A Multi-Omics Framework for Uncertainty-Aware Disease Subtyping
Wang, Chunjiang Yan, Rui Zhang, Kun Jiang, Zihang He, Zhiyang Tao, Xiaodong Zhou, S Kevin
Wang, Chunjiang
Yan, Rui
Zhang, Kun
Jiang, Zihang
He, Zhiyang
Tao, Xiaodong
Zhou, S Kevin
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Department
Machine Learning
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Conference proceeding
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Abstract
The integration of multi-omics data, including DNA methylation, mRNA expression, and miRNA profiles, is crucial for accurate disease subtyping and outcome prediction in complex disorders such as Alzheimer's disease and various cancers. However, the inherent heterogeneity and inconsistency among omics views present significant challenges for reliable data fusion. To address these issues, we propose KANTrust, a novel framework for trustworthy multi-omics classification that explicitly models both epistemic and aleatoric uncertainties. Our method combines a Kolmogorov-Arnold Network (KAN)enhanced robust representation module, a contrastive evidence consistency module, and an evidence-theoretic fusion module to achieve reliable multi-view integration. KANTrust adaptively highlights informative features within each omics modality, promotes semantic alignment across views, and quantifies uncertainty through a Dempster-Shafer framework. Experimental evaluations on four real-world biomedical datasets demonstrate that KANTrust consistently outperforms state-of-the-art methods in both binary and multi-class classification tasks. Code is available at https://github.com/wcj6/KANTrust.
Citation
C. Wang, R. Yan, K. Zhang, Z. Jiang, Z. He, X. Tao , et al., "KANTrust: A Multi-Omics Framework for Uncertainty-Aware Disease Subtyping," 2026, pp. 1290-1297.
Source
2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Conference
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
31 Biological Sciences, 46 Information and Computing Sciences, 4611 Machine Learning, 3 Good Health and Well Being
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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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IEEE