A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data.

ABSTRACT:

Background

Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher.

Results

Here we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN) structures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM challenge benchmark data. The highlight of our method is that its superior performance does not degenerat

SUBMITTER: Gui S 

PROVIDER: S-EPMC5294888 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature