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Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
BACKGROUND: Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framew...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433362/ https://www.ncbi.nlm.nih.gov/pubmed/22694481 http://dx.doi.org/10.1186/1471-2105-13-131 |
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author | Xuan, Nguyen Chetty, Madhu Coppel, Ross Wangikar, Pramod P |
author_facet | Xuan, Nguyen Chetty, Madhu Coppel, Ross Wangikar, Pramod P |
author_sort | Xuan, Nguyen |
collection | PubMed |
description | BACKGROUND: Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. RESULTS: To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT(+), employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT(+) is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. CONCLUSIONS: Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks. |
format | Online Article Text |
id | pubmed-3433362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34333622012-09-06 Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network Xuan, Nguyen Chetty, Madhu Coppel, Ross Wangikar, Pramod P BMC Bioinformatics Research Article BACKGROUND: Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. RESULTS: To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT(+), employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT(+) is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. CONCLUSIONS: Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks. BioMed Central 2012-06-13 /pmc/articles/PMC3433362/ /pubmed/22694481 http://dx.doi.org/10.1186/1471-2105-13-131 Text en Copyright ©2012 Vinh et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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. |
spellingShingle | Research Article Xuan, Nguyen Chetty, Madhu Coppel, Ross Wangikar, Pramod P Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network |
title | Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network |
title_full | Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network |
title_fullStr | Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network |
title_full_unstemmed | Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network |
title_short | Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network |
title_sort | gene regulatory network modeling via global optimization of high-order dynamic bayesian network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433362/ https://www.ncbi.nlm.nih.gov/pubmed/22694481 http://dx.doi.org/10.1186/1471-2105-13-131 |
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