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Inference of regulatory networks with a convergence improved MCMC sampler
BACKGROUND: One of the goals of the Systems Biology community is to have a detailed map of all biological interactions in an organism. One small yet important step in this direction is the creation of biological networks from post-genomic data. Bayesian networks are a very promising model for the in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581096/ https://www.ncbi.nlm.nih.gov/pubmed/26399857 http://dx.doi.org/10.1186/s12859-015-0734-6 |
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author | Agostinho, Nilzair B. Machado, Karina S. Werhli, Adriano V. |
author_facet | Agostinho, Nilzair B. Machado, Karina S. Werhli, Adriano V. |
author_sort | Agostinho, Nilzair B. |
collection | PubMed |
description | BACKGROUND: One of the goals of the Systems Biology community is to have a detailed map of all biological interactions in an organism. One small yet important step in this direction is the creation of biological networks from post-genomic data. Bayesian networks are a very promising model for the inference of regulatory networks in Systems Biology. Usually, Bayesian networks are sampled with a Markov Chain Monte Carlo (MCMC) sampler in the structure space. Unfortunately, conventional MCMC sampling schemes are often slow in mixing and convergence. To improve MCMC convergence, an alternative method is proposed and tested with different sets of data. Moreover, the proposed method is compared with the traditional MCMC sampling scheme. RESULTS: In the proposed method, a simpler and faster method for the inference of regulatory networks, Graphical Gaussian Models (GGMs), is integrated into the Bayesian network inference, trough a Hierarchical Bayesian model. In this manner, information about the structure obtained from the data with GGMs is taken into account in the MCMC scheme, thus improving mixing and convergence. The proposed method is tested with three types of data, two from simulated models and one from real data. The results are compared with the results of the traditional MCMC sampling scheme in terms of network recovery accuracy and convergence. The results show that when compared with a traditional MCMC scheme, the proposed method presents improved convergence leading to better network reconstruction with less MCMC iterations. CONCLUSIONS: The proposed method is a viable alternative to improve mixing and convergence of traditional MCMC schemes. It allows the use of Bayesian networks with an MCMC sampler with less iterations. The proposed method has always converged earlier than the traditional MCMC scheme. We observe an improvement in accuracy of the recovered networks for the Gaussian simulated data, but this improvement is absent for both real data and data simulated from ODE. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0734-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4581096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45810962015-09-25 Inference of regulatory networks with a convergence improved MCMC sampler Agostinho, Nilzair B. Machado, Karina S. Werhli, Adriano V. BMC Bioinformatics Research Article BACKGROUND: One of the goals of the Systems Biology community is to have a detailed map of all biological interactions in an organism. One small yet important step in this direction is the creation of biological networks from post-genomic data. Bayesian networks are a very promising model for the inference of regulatory networks in Systems Biology. Usually, Bayesian networks are sampled with a Markov Chain Monte Carlo (MCMC) sampler in the structure space. Unfortunately, conventional MCMC sampling schemes are often slow in mixing and convergence. To improve MCMC convergence, an alternative method is proposed and tested with different sets of data. Moreover, the proposed method is compared with the traditional MCMC sampling scheme. RESULTS: In the proposed method, a simpler and faster method for the inference of regulatory networks, Graphical Gaussian Models (GGMs), is integrated into the Bayesian network inference, trough a Hierarchical Bayesian model. In this manner, information about the structure obtained from the data with GGMs is taken into account in the MCMC scheme, thus improving mixing and convergence. The proposed method is tested with three types of data, two from simulated models and one from real data. The results are compared with the results of the traditional MCMC sampling scheme in terms of network recovery accuracy and convergence. The results show that when compared with a traditional MCMC scheme, the proposed method presents improved convergence leading to better network reconstruction with less MCMC iterations. CONCLUSIONS: The proposed method is a viable alternative to improve mixing and convergence of traditional MCMC schemes. It allows the use of Bayesian networks with an MCMC sampler with less iterations. The proposed method has always converged earlier than the traditional MCMC scheme. We observe an improvement in accuracy of the recovered networks for the Gaussian simulated data, but this improvement is absent for both real data and data simulated from ODE. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0734-6) contains supplementary material, which is available to authorized users. BioMed Central 2015-09-24 /pmc/articles/PMC4581096/ /pubmed/26399857 http://dx.doi.org/10.1186/s12859-015-0734-6 Text en © Agostinho et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Agostinho, Nilzair B. Machado, Karina S. Werhli, Adriano V. Inference of regulatory networks with a convergence improved MCMC sampler |
title | Inference of regulatory networks with a convergence improved MCMC sampler |
title_full | Inference of regulatory networks with a convergence improved MCMC sampler |
title_fullStr | Inference of regulatory networks with a convergence improved MCMC sampler |
title_full_unstemmed | Inference of regulatory networks with a convergence improved MCMC sampler |
title_short | Inference of regulatory networks with a convergence improved MCMC sampler |
title_sort | inference of regulatory networks with a convergence improved mcmc sampler |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581096/ https://www.ncbi.nlm.nih.gov/pubmed/26399857 http://dx.doi.org/10.1186/s12859-015-0734-6 |
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