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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Bibliographic Details
Published in	PloS one Vol. 5; no. 9; p. e12776
Main Authors	Huynh-Thu, Vân Anh, Irrthum, Alexandre, Wehenkel, Louis, Geurts, Pierre
Format	Journal Article
Language	English
Published	United States Public Library of Science 28.09.2010 Public Library of Science (PLoS)
Subjects	Acids Algorithms Analysis Artificial intelligence Benchmarks Biochemistry, biophysics & molecular biology Biochimie, biophysique & biologie moléculaire Bioinformatics Combinatorial analysis Computational Biology Computational Biology - methods Computational Biology/Systems Biology Computational Biology/Transcriptional Regulation Computer applications Computer engineering Computer science Computer simulation DNA microarrays E coli Electrical engineering Engineering, computing & technology Escherichia coli Escherichia coli - genetics Gene expression Gene Expression Regulation Gene regulation Gene Regulatory Networks Genes Genetic research Genomics Identification Inference Ingénierie, informatique & technologie Life sciences Machine learning Metadata Methods Oligonucleotide Array Sequence Analysis Ordinary differential equations Predictions Random variables Reverse engineering Sciences du vivant Sciences informatiques systems biology Topology Trees Belgium
Online Access	Get full text
ISSN	1932-6203 1932-6203
DOI	10.1371/journal.pone.0012776

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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Geurts, Pierre Huynh-Thu, Vân Anh Wehenkel, Louis Irrthum, Alexandre
AuthorAffiliation	1 Department of Electrical Engineering and Computer Science, Systems and Modeling, University of Liège, Liège, Belgium Center for Genomic Regulation, Spain 2 GIGA-Research, Bioinformatics and Modeling, University of Liège, Liège, Belgium
AuthorAffiliation_xml	– name: Center for Genomic Regulation, Spain – name: 2 GIGA-Research, Bioinformatics and Modeling, University of Liège, Liège, Belgium – name: 1 Department of Electrical Engineering and Computer Science, Systems and Modeling, University of Liège, Liège, Belgium
Author_xml	– sequence: 1 givenname: Vân Anh surname: Huynh-Thu fullname: Huynh-Thu, Vân Anh – sequence: 2 givenname: Alexandre surname: Irrthum fullname: Irrthum, Alexandre – sequence: 3 givenname: Louis surname: Wehenkel fullname: Wehenkel, Louis – sequence: 4 givenname: Pierre surname: Geurts fullname: Geurts, Pierre
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/20927193$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Contributor	Systems and modeling (Dept. of EE and CS) and Bioinformatics and modeling (GIGA-R)
Contributor_xml	– sequence: 1 fullname: Systems and modeling (Dept. of EE and CS) and Bioinformatics and modeling (GIGA-R)
Copyright	COPYRIGHT 2010 Public Library of Science 2010 Huynh-Thu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Huynh-Thu et al. 2010
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Snippet	One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high...
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Title	Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
URI	https://www.ncbi.nlm.nih.gov/pubmed/20927193 https://www.proquest.com/docview/1292466548 https://www.proquest.com/docview/757176875 http://orbi.ulg.ac.be/handle/2268/73256 https://pubmed.ncbi.nlm.nih.gov/PMC2946910 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0012776&type=printable https://doaj.org/article/13e2336da0c54cd497a5c68406a2a9ae http://dx.doi.org/10.1371/journal.pone.0012776
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