Multi-view multi-level contrastive graph convolutional network for cancer subtyping on multi-omics data.

ABSTRACT: Cancer is a highly diverse group of diseases, and each type of cancer can be further divided into various subtypes according to specific characteristics, cellular origins, and molecular markers. Subtyping helps in tailoring treatment and prognosis accuracy. However, the existing studies are more concerned with integrating different omics data to discover potential connections, but ignoring the relationships between consensus information and individual information within each omics level during the integration process. To this end, we propose a novel fusion-free method called multi-view multi-level contrastive graph convolutional network (M$^{2}$CGCN) for cancer subtyping. M$^{2}$CGCN learns multi-level features, i.e. high-level and low-level features, respectively. The low-level features f

SUBMITTER: Yang B 

PROVIDER: S-EPMC11789786 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature