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

BMC Bioinformatics
Peng LiYouping Deng

Abstract

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. 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. 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 ...Continue Reading

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Related Concepts

Embryo
Drosophila melanogaster Proteins
Candidate Disease Gene
Logistic Regression
Drosophila
Gene Expression
Larval Somatic Muscle Development
Data Interpretation, Statistical
Gene Regulatory Networks
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