Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes.

ABSTRACT: Combining path consistency (PC) algorithms with conditional mutual information (CMI) are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference), to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulat

SUBMITTER: Xiao F 

PROVIDER: S-EPMC4865039 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature