{"type":"ScholarlyArticle","title":"Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priors","authors":[{"name":"Jielong Lu","slug":"jielong-lu","given":"Jielong","family":"Lu","affiliation":""},{"name":"Zhihao Wu","slug":"zhihao-wu","given":"Zhihao","family":"Wu","affiliation":""},{"name":"Jiajun Yu","slug":"jiajun-yu","given":"Jiajun","family":"Yu","affiliation":""},{"name":"Jiajun Bu","slug":"jiajun-bu","given":"Jiajun","family":"Bu","affiliation":""},{"name":"Haishuai Wang","slug":"haishuai-wang","given":"Haishuai","family":"Wang","affiliation":""}],"abstract":"Integrating multi-omics datasets through data-driven analysis offers a comprehensive understanding of the complex biological processes underlying various diseases, particularly cancer.\r\nGraph Neural Networks (GNNs) have recently demonstrated remarkable ability to exploit relational structures in biological data, enabling advances in multi-omics integration for cancer subtype classification. \r\nExisting approaches often neglect the intricate coupling between heterogeneous omics, limiting their capacity to resolve subtle cancer subtype heterogeneity critical for precision oncology.\r\nTo address these limitations, we propose a framework named Graph Transformer for Multi-omics Cancer Subtype Classification (GTMancer).\r\nThis framework builds upon the GNN optimization problem and extends its application to complex multi-omics data.\r\nSpecifically, our method leverages contrastive learning to embed multi-omics data into a unified semantic space.\r\nWe unroll the multiplex graph optimization problem in that unified space and introduce dual sets of attention coefficients to capture structural graph priors both within and among multi-omics data.\r\nThis approach enables global omics information to guide the refining of the representations of individual omics.\r\nEmpirical experiments on seven real-world cancer datasets demonstrate that GTMancer outperforms existing state-of-the-art algorithms.","doi":"10.24963/ijcai.2025/650","url":"https://ijcai.org/proceedings/2025/650","pdf":"https://ijcai.org/proceedings/2025/0650.pdf","markdown":"https://ijcai.org/proceedings/2025/650.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/650","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":650,"pages":{"first":5842,"last":5850},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}