用置信度指导多组学图学习，提升癌症分型准确率
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
- 先用证据深度学习估算每样本各组学可靠性，再固定分数用于融合与建图
- 在4个癌种任务上平均准确率提升4.03%，优于最强基线
- 可跨癌种迁移，无需微调即可区分肾癌患者预后差异
多组学整合可提升癌症分型效果，但不同癌种和患者间组学信息量与噪声差异大。现有图方法将模态权重与分类目标联合优化，缺乏独立的可靠性评估，导致低质量组学扭曲患者相似性图并放大噪声传播。本文提出CMGL，一种两阶段框架：先通过证据深度学习估计每样本各组学的置信度，再冻结这些置信度用于跨组学融合与图构建。在四个MLOmics癌种分型任务及32类泛癌任务中，CMGL持续优于最强基线，在四个单癌任务上平均准确率提升4.03%。其表示能恢复乳腺浸润性癌（BRCA）的PAM50固有亚型，且在BRCA上训练的模型无需微调即可迁移到肾透明细胞癌（KIRC），将患者分为具有显著预后的组别。
原文摘要 · Abstract (English)
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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