CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification

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ID: 332221
2026
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Abstract
MOTIVATION: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients. Most graph methods for multi-omics data learn modality contributions within the downstream classification objective, leaving predictive reliability for each patient implicit. As a result, uninformative modalities can weaken the fused representation, while unreliable omics can introduce noisy patient relationships into graph propagation. To address these two problems, we propose CMGL, which produces a separate reliability estimate before fusion and uses consensus patient neighborhoods for graph classification.
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openalex_W7156622458 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Boyang Fan, Hengchuang Yin, Siyu Yi, Yifan Wang, Zhicheng Li, Leijiyu Zhou, Jiancheng Lv, Wei Ju
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag763
URL
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag763/71892484/btag763.pdf
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