数字农科院2.0

Multi-relational multi-view clustering and its applications in cancer subtype identification

文献类型： 外文期刊

作者： Zhang, Chao;Xu, Deng;Chen, Chunlin;Zhang, Min;Li, Huaxiong

作者机构：

关键词： Multi-view clustering;Multi-level relations;Similarity learning;Cancer subtype identification

期刊名称： INFORMATION FUSION

ISSN： 1566-2535

年卷期： 2025 年 117 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Cancer subtype identification aims to partition the cancer patients into different subgroups with distinct clinical phenotypes, which is important for accurate diagnosis and treatment planning. The recent surge in multi-omics data has spurred research into integrative subtype identification, and multi-view clustering is widely used for identifying the underlying potential subtypes in an unsupervised manner. However, most existing approaches only consider the single-level relations within each view and cannot fully explore the high-order relations across views. In this paper, we propose anew Multi-Relational Multi-View Clustering (MRMVC) method to address these issues, which treats multi-omics data as different views, and thoroughly explores multi-level intra-view and inter-view relations for subtype identification. It fully learns the pairwise similarity between samples based on (1) intra-view global relations that encourage the intra-class cohesion, (2) intra-view local relations that promote the inter-class separability, and (3) inter-view high-order relations that align the multiple graphs, enabling the discovery of intrinsic similarities and enhancing the clustering performance. Experiments on generic datasets and multi-omics cancer datasets illustrate the efficacy and superiority of the proposed method in clustering and identifying more distinct cancer subtypes.

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