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

BMC Systems Biology

Ka Yee Yeung³  Roger E Bumgarner³  Eric E Schadt¹  Jun Zhu¹  Kenneth M Dombek⁴  Adrian E Raftery²  Kenneth Lo³ 

[1] Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, 10029, USA;Department of Statistics, University of Washington, Box 354320, Seattle, WA, 98195, USA;Department of Microbiology, University of Washington, Box 358070, Seattle, WA, 98195, USA;Department of Biochemistry, University of Washington, Box 357350, Seattle, WA, 98195, USA

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

Others  :  1143709
DOI  :  10.1186/1752-0509-6-101

 received in 2012-01-25, accepted in 2012-07-24,  发布年份 2012

【 摘 要 】

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.

【 授权许可】

   
2012 Lo et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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