RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no...

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Autores principales:	Marco Grimaldi, Roberto Visintainer, Giuseppe Jurman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/69fc43ad1ff94d968eead479b26fa5a8
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spelling	oai:doaj.org-article:69fc43ad1ff94d968eead479b26fa5a82021-11-18T07:31:26ZRegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.1932-620310.1371/journal.pone.0028646https://doaj.org/article/69fc43ad1ff94d968eead479b26fa5a82011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22216103/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.Marco GrimaldiRoberto VisintainerGiuseppe JurmanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 12, p e28646 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marco Grimaldi Roberto Visintainer Giuseppe Jurman RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.
description	RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.
format	article
author	Marco Grimaldi Roberto Visintainer Giuseppe Jurman
author_facet	Marco Grimaldi Roberto Visintainer Giuseppe Jurman
author_sort	Marco Grimaldi
title	RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.
title_short	RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.
title_full	RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.
title_fullStr	RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.
title_full_unstemmed	RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.
title_sort	regnann: reverse engineering gene networks using artificial neural networks.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/69fc43ad1ff94d968eead479b26fa5a8
work_keys_str_mv	AT marcogrimaldi regnannreverseengineeringgenenetworksusingartificialneuralnetworks AT robertovisintainer regnannreverseengineeringgenenetworksusingartificialneuralnetworks AT giuseppejurman regnannreverseengineeringgenenetworksusingartificialneuralnetworks
_version_	1718423349254684672

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

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