Prior knowledge driven Granger causality analysis on gene regulatory network discovery.

ABSTRACT: BACKGROUND:Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. RESULTS:In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC model

SUBMITTER: Yao S 

PROVIDER: S-EPMC4551367 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature