Integrating external biological knowledge in the construction of regulatory networks from time-series expression data.

ABSTRACT:

Background

Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge.

Results

We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression mo

SUBMITTER: Lo K 

PROVIDER: S-EPMC3465231 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature