AI RESEARCH
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
arXiv CS.LG
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ArXi:2604.24201v1 Announce Type: new Motivation: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients. Existing graph-based methods optimize modality weights jointly with the classification objective and. therefore. lack independent reliability estimates, so low-quality omics distort patient similarity graphs and amplify noise through message passing.