Inferring regulatory networks from expression data using tree-based methods.

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challeng...

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Autores principales:	Vân Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, Pierre Geurts
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/13e2336da0c54cd497a5c68406a2a9ae
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spelling	oai:doaj.org-article:13e2336da0c54cd497a5c68406a2a9ae2021-11-18T07:03:54ZInferring regulatory networks from expression data using tree-based methods.1932-620310.1371/journal.pone.0012776https://doaj.org/article/13e2336da0c54cd497a5c68406a2a9ae2010-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20927193/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.Vân Anh Huynh-ThuAlexandre IrrthumLouis WehenkelPierre GeurtsPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 9 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vân Anh Huynh-Thu Alexandre Irrthum Louis Wehenkel Pierre Geurts Inferring regulatory networks from expression data using tree-based methods.
description	One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.
format	article
author	Vân Anh Huynh-Thu Alexandre Irrthum Louis Wehenkel Pierre Geurts
author_facet	Vân Anh Huynh-Thu Alexandre Irrthum Louis Wehenkel Pierre Geurts
author_sort	Vân Anh Huynh-Thu
title	Inferring regulatory networks from expression data using tree-based methods.
title_short	Inferring regulatory networks from expression data using tree-based methods.
title_full	Inferring regulatory networks from expression data using tree-based methods.
title_fullStr	Inferring regulatory networks from expression data using tree-based methods.
title_full_unstemmed	Inferring regulatory networks from expression data using tree-based methods.
title_sort	inferring regulatory networks from expression data using tree-based methods.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/13e2336da0c54cd497a5c68406a2a9ae
work_keys_str_mv	AT vananhhuynhthu inferringregulatorynetworksfromexpressiondatausingtreebasedmethods AT alexandreirrthum inferringregulatorynetworksfromexpressiondatausingtreebasedmethods AT louiswehenkel inferringregulatorynetworksfromexpressiondatausingtreebasedmethods AT pierregeurts inferringregulatorynetworksfromexpressiondatausingtreebasedmethods
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Inferring regulatory networks from expression data using tree-based methods.

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