{"type":"ScholarlyArticle","title":"Divide and Conquer: Coordinating Multiplex Mixture of Graph Learners to Handle Multi-Omics Analysis","authors":[{"name":"Zhihao Wu","slug":"zhihao-wu","given":"Zhihao","family":"Wu","affiliation":""},{"name":"Jielong Lu","slug":"jielong-lu","given":"Jielong","family":"Lu","affiliation":""},{"name":"Jiajun Yu","slug":"jiajun-yu","given":"Jiajun","family":"Yu","affiliation":""},{"name":"Sheng Zhou","slug":"sheng-zhou","given":"Sheng","family":"Zhou","affiliation":""},{"name":"Yueyang Pi","slug":"yueyang-pi","given":"Yueyang","family":"Pi","affiliation":""},{"name":"Haishuai Wang","slug":"haishuai-wang","given":"Haishuai","family":"Wang","affiliation":""}],"abstract":"Graph learning has shown significant advantages in organizing and leveraging complex data, making it promising for numerous real-world applications with heterogeneous information, particularly multi-omics data analysis. Despite its potential in such scenarios, existing methods are still in their infancy, lacking architectural potential and struggling to handle such complex data. In this paper, we propose the Multiplex Mixture of Graph Learners (MMoG) framework. MMoG first conducts fine-grained processing of consensus and unique information, constructing consistent features and multiplex graph structures. Then, a macroscopically shared group of sub-GNNs with diverse orders and architectures synergistically learn representations, providing a foundation for strong interaction between different views. Inspired by the mixture of experts (MoE), each sample in different omics adaptively determines the neighborhood ranges and architectures for information aggregation, while blocking unsuitable sub-GNNs. MMoG treats the complex multi-omics analysis as a multi-view learning problem, and essentially decomposes it into multiple sub-problems, allowing each omics/view to solve intersecting yet unique sub-problem groups. Additionally, we introduce mutual information-driven orthogonal loss and balancing loss to avoid view collapse. Extensive experiments on multi-omics data across multiple cancer types highlight MMoG's superiority.","doi":"10.24963/ijcai.2025/736","url":"https://ijcai.org/proceedings/2025/736","pdf":"https://ijcai.org/proceedings/2025/0736.pdf","markdown":"https://ijcai.org/proceedings/2025/736.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/736","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":736,"pages":{"first":6615,"last":6623},"keywords":["Machine Learning: ML: Deep learning architectures","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}