Fast Bayesian inference for gene regulatory networks using ScanBMA.

ABSTRACT:

Background

Genome-wide time-series data provide a rich set of information for discovering gene regulatory relationships. As genome-wide data for mammalian systems are being generated, it is critical to develop network inference methods that can handle tens of thousands of genes efficiently, provide a systematic framework for the integration of multiple data sources, and yield robust, accurate and compact gene-to-gene relationships.

Results

We developed and applied ScanBMA, a Bayesian inference method that incorporates external information to improve the accuracy of the inferred network. In particular, we developed a new strategy to efficiently search the model space, applied data transformations to reduce the effect of spurious relationships, and adopted the g-prior to guide

SUBMITTER: Young WC 

PROVIDER: S-EPMC4006459 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature