CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908995230367744 |
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| author | Fan, Boyang Yin, Hengchuang Yi, Siyu Wang, Yifan Li, Zhicheng Zhou, Leijiyu Lv, Jiancheng Ju, Wei |
| author_facet | Fan, Boyang Yin, Hengchuang Yi, Siyu Wang, Yifan Li, Zhicheng Zhou, Leijiyu Lv, Jiancheng Ju, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24201 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification Fan, Boyang Yin, Hengchuang Yi, Siyu Wang, Yifan Li, Zhicheng Zhou, Leijiyu Lv, Jiancheng Ju, Wei Machine Learning Genomics Molecular Networks 62H30, 68T07, 92C40 I.2.6; J.3 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. |
| title | CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification |
| topic | Machine Learning Genomics Molecular Networks 62H30, 68T07, 92C40 I.2.6; J.3 |
| url | https://arxiv.org/abs/2604.24201 |