Hub-centered gene network reconstruction using automatic relevance determination.

Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity...

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Autores principales:	Matthias Böck, Soichi Ogishima, Hiroshi Tanaka, Stefan Kramer, Lars Kaderali
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eda7a9371f72439f876de318fc6aea6d
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spelling	oai:doaj.org-article:eda7a9371f72439f876de318fc6aea6d2021-11-18T07:19:50ZHub-centered gene network reconstruction using automatic relevance determination.1932-620310.1371/journal.pone.0035077https://doaj.org/article/eda7a9371f72439f876de318fc6aea6d2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22570688/?tool=EBIhttps://doaj.org/toc/1932-6203Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data.Matthias BöckSoichi OgishimaHiroshi TanakaStefan KramerLars KaderaliPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 5, p e35077 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthias Böck Soichi Ogishima Hiroshi Tanaka Stefan Kramer Lars Kaderali Hub-centered gene network reconstruction using automatic relevance determination.
description	Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data.
format	article
author	Matthias Böck Soichi Ogishima Hiroshi Tanaka Stefan Kramer Lars Kaderali
author_facet	Matthias Böck Soichi Ogishima Hiroshi Tanaka Stefan Kramer Lars Kaderali
author_sort	Matthias Böck
title	Hub-centered gene network reconstruction using automatic relevance determination.
title_short	Hub-centered gene network reconstruction using automatic relevance determination.
title_full	Hub-centered gene network reconstruction using automatic relevance determination.
title_fullStr	Hub-centered gene network reconstruction using automatic relevance determination.
title_full_unstemmed	Hub-centered gene network reconstruction using automatic relevance determination.
title_sort	hub-centered gene network reconstruction using automatic relevance determination.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/eda7a9371f72439f876de318fc6aea6d
work_keys_str_mv	AT matthiasbock hubcenteredgenenetworkreconstructionusingautomaticrelevancedetermination AT soichiogishima hubcenteredgenenetworkreconstructionusingautomaticrelevancedetermination AT hiroshitanaka hubcenteredgenenetworkreconstructionusingautomaticrelevancedetermination AT stefankramer hubcenteredgenenetworkreconstructionusingautomaticrelevancedetermination AT larskaderali hubcenteredgenenetworkreconstructionusingautomaticrelevancedetermination
_version_	1718423636505788416

Hub-centered gene network reconstruction using automatic relevance determination.

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