Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism
dc.contributor.author | Jia, Yi | |
dc.contributor.author | Huan, Luke | |
dc.date.accessioned | 2015-11-13T20:15:44Z | |
dc.date.available | 2015-11-13T20:15:44Z | |
dc.date.issued | 2010-10-07 | |
dc.identifier.citation | Jia, Yi, and Jun Huan. "Constructing Non-stationary Dynamic Bayesian Networks with a Flexible Lag Choosing Mechanism." BMC Bioinformatics 11.Suppl 6 (2010). http://dx.doi.org/10.1186/1471-2105-11-S6-S27 | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/18904 | |
dc.description.abstract | BackgroundDynamic Bayesian Networks (DBNs) are widely used in regulatory network structure inference with gene expression data. Current methods assumed that the underlying stochastic processes that generate the gene expression data are stationary. The assumption is not realistic in certain applications where the intrinsic regulatory networks are subject to changes for adapting to internal or external stimuli. ResultsIn this paper we investigate a novel non-stationary DBNs method with a potential regulator detection technique and a flexible lag choosing mechanism. We apply the approach for the gene regulatory network inference on three non-stationary time series data. For the Macrophages and Arabidopsis data sets with the reference networks, our method shows better network structure prediction accuracy. For the Drosophila data set, our approach converges faster and shows a better prediction accuracy on transition times. In addition, our reconstructed regulatory networks on the Drosophila data not only share a lot of similarities with the predictions of the work of other researchers but also provide many new structural information for further investigation. ConclusionsCompared with recent proposed non-stationary DBNs methods, our approach has better structure prediction accuracy By detecting potential regulators, our method reduces the size of the search space, hence may speed up the convergence of MCMC sampling. | en_US |
dc.publisher | BioMed Central | en_US |
dc.rights | Copyright © 2010 Huan and Jia; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
dc.rights.uri | http://creativecommons.org/licenses/by/2.0/ | |
dc.title | Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism | en_US |
dc.type | Article | |
kusw.kuauthor | Huan, Luke | |
kusw.kudepartment | Electrical Engr & Comp Science | en_US |
dc.identifier.doi | 10.1186/1471-2105-11-S6-S27 | |
kusw.oaversion | Scholarly/refereed, publisher version | |
kusw.oapolicy | This item meets KU Open Access policy criteria. | |
dc.rights.accessrights | openAccess |
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Except where otherwise noted, this item's license is described as: Copyright © 2010 Huan and Jia; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.