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arXiv · 2604.24201

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

Boyang Fan·Hengchuang Yin·Siyu Yi·Yifan Wang·Zhicheng Li·Leijiyu Zhou·Jiancheng Lv·Wei Ju
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Abstract

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. Results: We propose CMGL, a two-stage framework that estimates per-sample modality reliability through evidential deep learning and uses the frozen confidence scores to guide cross-omics fusion and graph construction. On four MLOmics cancer-subtype tasks and the 32-class pan-cancer task, CMGL consistently improves over the strongest baseline, surpassing it by 4.03% in average accuracy on the four single-cancer tasks. Its representations recover the PAM50 intrinsic subtypes of breast invasive carcinoma (BRCA), and the BRCA-trained model transfers without fine-tuning to kidney renal clear cell carcinoma (KIRC), stratifying patients into prognostically distinct groups.

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Boyang Fan, Hengchuang Yin, Siyu Yi, Yifan Wang, Zhicheng Li, Leijiyu Zhou, Jiancheng Lv, Wei Ju. 2026-04-27. CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification. https://arxiv.org/abs/2604.24201

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