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


CONCLUSIONS
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.
School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA. peng.li@usm.edu
Peng
P
Li
Chaoyang
C
Zhang
Edward J
EJ
Perkins
Ping
P
Gong
Youping
Y
Deng
D000818:Animals; D003627:Data Interpretation, Statistical; D004330:Drosophila / Q000378:metabolism*; D029721:Drosophila Proteins / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016015:Logistic Models; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015398:Signal Transduction / Q000502:physiology*
D029721:Drosophila Proteins