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arXiv Machine Learning By Boyang Fan, Hengchuang Yin, Siyu Yi, Yifan Wang, Zhicheng Li, Leijiyu Zhou, Jiancheng Lv, Wei Ju

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

Read the original on arXiv Machine Learning →

CMGL is a confidence‑guided multi‑omics graph learning framework that first estimates modality confidence for each patient using evidential deep learning, then fuses omics data with fixed confidence values and performs classification on a consistency graph. On four cancer‑subtype tasks and a 32‑class pan‑cancer task, CMGL outperforms the strongest baseline by an average of 4.03% accuracy. Its learned representations recover known breast cancer subtypes and transfer to kidney cancer, enabling prognostically distinct patient stratification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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