NEMO: cancer subtyping by integration of partial multi-omic data.

ABSTRACT: MOTIVATION:Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. RESULTS:We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an im

SUBMITTER: Rappoport N 

PROVIDER: S-EPMC6748715 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature