Abstract
BackgroundDynamic Bayesian Networks (DBNs) are widely used in regulatory network structure inference with gene expression data. Current methods assumed that the underlying stochastic processes that generate the gene expression data are stationary. The assumption is not realistic in certain applications where the intrinsic regulatory networks are subject to changes for adapting to internal or external stimuli.ResultsIn this paper we investigate a novel non-stationary DBNs method with a potential regulator detection technique and a flexible lag choosing mechanism. We apply the approach for the gene regulatory network inference on three non-stationary time series data. For the Macrophages and Arabidopsis data sets with the reference networks, our method shows better network structure prediction accuracy. For the Drosophila data set, our approach converges faster and shows a better prediction accuracy on transition times. In addition, our reconstructed regulatory networks on the Drosophila data not only share a lot of similarities with the predictions of the work of other researchers but also provide many new structural information for further investigation.ConclusionsCompared with recent proposed non-stationary DBNs methods, our approach has better structure prediction accuracy By detecting potential regulators, our method reduces the size of the search space, hence may speed up the convergence of MCMC sampling.
Highlights
ResultsIn this paper we investigate a novel non-stationary Dynamic Bayesian Networks (DBNs) method with a potential regulator detection technique and a flexible lag choosing mechanism
Non-stationary Bayesian network models have attracted significant research interests in modeling gene expression data
Experimental results we compare the experimental results of three approaches: FLnsDBNs, RJnsDBNs, and ASnsDBNs on three data sets
Summary
In this paper we investigate a novel non-stationary DBNs method with a potential regulator detection technique and a flexible lag choosing mechanism. We apply the approach for the gene regulatory network inference on three non-stationary time series data. For the Macrophages and Arabidopsis data sets with the reference networks, our method shows better network structure prediction accuracy. For the Drosophila data set, our approach converges faster and shows a better prediction accuracy on transition times. Our reconstructed regulatory networks on the Drosophila data share a lot of similarities with the predictions of the work of other researchers and provide many new structural information for further investigation
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