INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery.

ABSTRACT: Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that

SUBMITTER: Zuo Y 

PROVIDER: S-EPMC5135617 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature