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This article is part of the supplement: Proceedings of the Fourth Annual MCBIOS Conference. Computational Frontiers in Biomedicine

Open Access Proceedings

Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks

Peng Li1, Chaoyang Zhang1*, Edward J Perkins2, Ping Gong3 and Youping Deng4*

Author Affiliations

1 School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA

2 Environmental Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Rd. Vicksburg, MS, 39180, USA

3 SpecPro Inc., 3909 Halls Ferry Rd, Vicksburg, MS, 39180, USA

4 Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA

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BMC Bioinformatics 2007, 8(Suppl 7):S13  doi:10.1186/1471-2105-8-S7-S13

Published: 1 November 2007

Abstract

Background

The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory network context. Several computational approaches are available for modeling gene regulatory networks with different datasets. In order to optimize modeling of GRN, these approaches must be compared and evaluated in terms of accuracy and efficiency.

Results

In this paper, two important computational approaches for modeling gene regulatory networks, probabilistic Boolean network methods and dynamic Bayesian network methods, are compared using a biological time-series dataset from the Drosophila Interaction Database to construct a Drosophila gene network. A subset of time points and gene samples from the whole dataset is used to evaluate the performance of these two approaches.

Conclusion

The comparison indicates that both approaches had good performance in modeling the gene regulatory networks. The accuracy in terms of recall and precision can be improved if a smaller subset of genes is selected for inferring GRNs. The accuracy of both approaches is dependent upon the number of selected genes and time points of gene samples. In all tested cases, DBN identified more gene interactions and gave better recall than PBN.