The assembly of miRNA-mRNA-protein regulatory networks using high-throughput expression data.

ABSTRACT:

Motivation

Inference of gene regulatory networks from high throughput measurement of gene and protein expression is particularly attractive because it allows the simultaneous discovery of interactive molecular signals for numerous genes and proteins at a relatively low cost.

Results

We developed two score-based local causal learning algorithms that utilized the Markov blanket search to identify direct regulators of target mRNAs and proteins. These two algorithms were specifically designed for integrated high throughput RNA and protein data. Simulation study showed that these algorithms outperformed other state-of-the-art gene regulatory network learning algorithms. We also generated integrated miRNA, mRNA, and protein expression data based on high throughput analysis of prim

SUBMITTER: Chu T 

PROVIDER: S-EPMC4443676 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature