Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern.

ABSTRACT:

Background

Post-genome era brings about diverse categories of omics data. Inference and analysis of genetic regulatory networks act prominently in extracting inherent mechanisms, discovering and interpreting the related biological nature and living principles beneath mazy phenomena, and eventually promoting the well-beings of humankind.

Results

A supervised combinatorial-optimization pattern based on information and signal-processing theories is introduced into the inference and analysis of genetic regulatory networks. An associativity measure is proposed to define the regulatory strength/connectivity, and a phase-shift metric determines regulatory directions among components of the reconstructed networks. Thus, it solves the undirected regulatory problems arising from most

SUBMITTER: Tang B 

PROVIDER: S-EPMC2982690 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature