Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism

BMC Bioinformatics

Proceedings

Yi Jia¹  Jun Huan¹ 

[1] Department of Electrical Engineering & Computer Science, University of Kansas, 66045, Lawrence, KS, USA;

关键词: Posterior Probability;    Gene Expression Data;    Bayesian Network;    Dynamic Bayesian Network;    Gene Regulatory Network Inference;   

DOI  :  10.1186/1471-2105-11-S6-S27

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Huan and Jia; licensee BioMed Central Ltd. 2010