Integrating multidimensional data for clustering analysis with applications to cancer patient data.

ABSTRACT: Advances in high-throughput genomic technologies coupled with large-scale studies including The Cancer Genome Atlas (TCGA) project have generated rich resources of diverse types of omics data to better understand cancer etiology and treatment responses. Clustering patients into subtypes with similar disease etiologies and/or treatment responses using multiple omics data types has the potential to improve the precision of clustering than using a single data type. However, in practice, patient clustering is still mostly based on a single type of omics data or ad hoc integration of clustering results from individual data types, leading to potential loss of information. By treating each omics data type as a different informative representation from patients, we propose a novel multi-view spect

SUBMITTER: Park S 

PROVIDER: S-EPMC9634961 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature