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

@article{citeulike:2098426, 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.}, author = {Li, Peng and Zhang, Chaoyang and Perkins, Edward and Gong, Ping and Deng, Youping}, citeulike-article-id = {2098426}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-8-S7-S13}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18047712}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18047712}, doi = {10.1186/1471-2105-8-S7-S13}, journal = {BMC Bioinformatics}, keywords = {bayesian, boolean, dynamic, network, pbn, probabilistic}, number = {Suppl 7}, posted-at = {2009-03-25 18:45:59}, priority = {3}, title = {Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks}, url = {http://dx.doi.org/10.1186/1471-2105-8-S7-S13}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:2098426 L3 - citeulike-article-id:2098426 N2 - 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. IS - Suppl 7 JF - BMC Bioinformatics TI - Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks VL - 8 KW - bayesian KW - boolean KW - dynamic KW - network KW - pbn KW - probabilistic AU - Li, Peng AU - Zhang, Chaoyang AU - Perkins, Edward AU - Gong, Ping AU - Deng, Youping PY - 2007/// UR - http://dx.doi.org/10.1186/1471-2105-8-S7-S13 ER -