Applying dynamic Bayesian networks to perturbed gene expression data Export

@article{citeulike:1325516, abstract = {BACKGROUND:A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments.RESULTS:We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed.CONCLUSION:We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.}, address = {Institute of Informatics, Warsaw University, Banacha 2, 02-097 Warszawa, Poland. dojer@mimuw.edu.pl}, author = {Dojer, Norbert and Gambin, Anna and Mizera, Andrzej and Wilczynski, Bartek and Tiuryn, Jerzy}, citeulike-article-id = {1325516}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-7-249}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/16681847}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=16681847}, doi = {10.1186/1471-2105-7-249}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {bn, networks, perturbed}, number = {1}, pages = {249+}, posted-at = {2007-05-24 18:31:30}, priority = {3}, title = {Applying dynamic Bayesian networks to perturbed gene expression data}, url = {http://dx.doi.org/10.1186/1471-2105-7-249}, volume = {7}, year = {2006} }

TY - JOUR ID - citeulike:1325516 L3 - citeulike-article-id:1325516 N2 - BACKGROUND:A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments.RESULTS:We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed.CONCLUSION:We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 AD - Institute of Informatics, Warsaw University, Banacha 2, 02-097 Warszawa, Poland. dojer@mimuw.edu.pl TI - Applying dynamic Bayesian networks to perturbed gene expression data VL - 7 SP - 249 KW - bn KW - networks KW - perturbed AU - Dojer, Norbert AU - Gambin, Anna AU - Mizera, Andrzej AU - Wilczynski, Bartek AU - Tiuryn, Jerzy PY - 2006/// UR - http://dx.doi.org/10.1186/1471-2105-7-249 ER -