Spectral clustering using Nystrom approximation for the accurate identification of cancer molecular subtypes.

ABSTRACT: A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for molecular subtype discovery. In this analysis, we hypothesize that spectral clustering method could identify molecular subtypes in correlation with survival outcomes. We propose an accurate subtype identification method, Cancer Subtype Identification with Spectral Clustering using Nyström approximation (CSISCN), for the discovery of molecular subtypes, based on spectral clustering method. CSISCN could be used to improve gene expression-based identification of breast cancer molecular subtypes. We demonstrated that CSISCN identified the molec

SUBMITTER: Shi M 

PROVIDER: S-EPMC5501792 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature