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BMC Syst Biol. 2012; 6: 101.
Published online Aug 16, 2012. doi:  10.1186/1752-0509-6-101
PMCID: PMC3465231
Integrating external biological knowledge in the construction of regulatory networks from time-series expression data
Kenneth Lo,1 Adrian E Raftery,2 Kenneth M Dombek,3 Jun Zhu,4 Eric E Schadt,4 Roger E Bumgarner,1 and Ka Yee Yeungcorresponding author1
1Department of Microbiology, University of Washington, Box 358070, Seattle, WA, 98195, USA
2Department of Statistics, University of Washington, Box 354320, Seattle, WA, 98195, USA
3Department of Biochemistry, University of Washington, Box 357350, Seattle, WA, 98195, USA
4Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, 10029, USA
corresponding authorCorresponding author.
Kenneth Lo: kenchlo2/at/gmail.com; Adrian E Raftery: raftery/at/u.washington.edu; Kenneth M Dombek: kmd/at/u.washington.edu; Jun Zhu: jun.zhu/at/mssm.edu; Eric E Schadt: eric.schadt/at/gmail.com; Roger E Bumgarner: rogerb/at/u.washington.edu; Ka Yee Yeung: kayee/at/u.washington.edu
Received January 25, 2012; Accepted July 24, 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
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