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Open Access Methodology article

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

Kenneth Lo1, Adrian E Raftery2, Kenneth M Dombek3, Jun Zhu4, Eric E Schadt4, Roger E Bumgarner1 and Ka Yee Yeung1*

Author Affiliations

1 Department of Microbiology, University of Washington, Box 358070, Seattle, WA, 98195, USA

2 Department of Statistics, University of Washington, Box 354320, Seattle, WA, 98195, USA

3 Department of Biochemistry, University of Washington, Box 357350, Seattle, WA, 98195, USA

4 Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, 10029, USA

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BMC Systems Biology 2012, 6:101  doi:10.1186/1752-0509-6-101

Published: 16 August 2012

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 models.

Conclusions

We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.

Keywords:
Systems biology; Network inference; Data integration; Statistics; Time-series expression data; Model uncertainty