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• W2015271344 abstract "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." @default.
• W2015271344 created "2016-06-24" @default.
• W2015271344 creator A5009717062 @default.
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• W2015271344 creator A5064414755 @default.
• W2015271344 creator A5071946434 @default.
• W2015271344 date "2012-08-16" @default.
• W2015271344 modified "2023-09-27" @default.
• W2015271344 title "Integrating external biological knowledge in the construction of regulatory networks from time-series expression data" @default.
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• W2015271344 doi "https://doi.org/10.1186/1752-0509-6-101" @default.
• W2015271344 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3465231" @default.

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