Abstract
BackgroundInference 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.ResultsWe 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.ConclusionsWe 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.
Highlights
Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology
We applied our method, Iterative Bayesian model averaging (iBMA)-prior, to a time-series data set of gene expression levels for 95 genotyped haploid yeast segregants perturbed with the macrolide drug rapamycin over 6 time points [3]
To evaluate the performance of iBMA-prior, other published regression-based network construction methods were applied to the same time-series gene expression data set and the resulting networks were assessed for the recovery of documented regulatory relationships that were not used in the network construction process
Summary
Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. The construction of regulatory networks is essential for defining the interactions between genes and gene products, and predictive models may be used to develop novel therapies [1,2] Both microarrays and more recently generation sequencing provide the ability to quantify the expression levels of all genes in a given genome. The causal relationship is statistically inferred, in contrast to the classic definition of causality used in biology to imply direct physical interaction leading to a phenotypic change This is a challenging problem, especially on a genome-wide scale, since the goal is to unravel a small number of regulators (parent nodes) out of thousands of candidate nodes in the graph. In order to improve network inference, one would like a coherent approach to integrate external knowledge and data to both fill in gaps in the gene expression data and to constrain or guide the network search
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