MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning.

ABSTRACT: Cancer is an extremely complex disease and each type of cancer usually has several different subtypes. Multi-omics data can provide more comprehensive biological information for identifying and discovering cancer subtypes. However, existing unsupervised cancer subtyping methods cannot effectively learn comprehensive shared and specific information of multi-omics data. Therefore, a novel method is proposed based on shared and specific representation learning. For each omics data, two autoencoders are applied to extract shared and specific information, respectively. To reduce redundancy and mutual interference, orthogonality constraint is introduced to separate shared and specific information. In addition, contrastive learning is applied to align the shared information and strengthen their c

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10407241 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature