Decoupled contrastive multi-view clustering with adaptive false negative elimination for cancer subtyping.

ABSTRACT: Cancer's heterogeneity necessitates precise subtype identification for effective diagnosis and treatment, which can be achieved by integrating multi-omics data to reveal distinct molecular characteristics and enable personalized therapies. Recently, significant efforts have been made through contrastive clustering methods to efficiently identify cancer subtypes. However, existing approaches remain limited in effectively capturing inter- and intra-view relationships in multi-omics data. Additionally, most cancer subtyping methods often rely on random sampling to construct negative pairs, which may inadvertently engender false negatives. To overcome these challenges, we propose a novel end-to-end self-supervised learning model named Decoupled Contrastive Multi-view Clustering with adaptive f

SUBMITTER: Lin M 

PROVIDER: S-EPMC12711033 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature