Functional association networks as priors for gene regulatory network inference.

ABSTRACT:

Motivation

Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences.

Results

This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on y

SUBMITTER: Studham ME 

PROVIDER: S-EPMC4058914 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature