CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
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. Results: CMGL estimates modality confi...
| Authors | Boyang Fan, Hengchuang Yin, Siyu Yi et al. |
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| Primary category | cs.LG |
| Published | 27 April 2026 |
| Last updated at source | 8 October 2026 |
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