Teamwork: improved eQTL mapping using combinations of machine learning methods.

Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initi...

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Autores principales:	Marit Ackermann, Mathieu Clément-Ziza, Jacob J Michaelson, Andreas Beyer
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0a71e1d33a424c78b7914b17a5b41f99
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id	oai:doaj.org-article:0a71e1d33a424c78b7914b17a5b41f99
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spelling	oai:doaj.org-article:0a71e1d33a424c78b7914b17a5b41f992021-11-18T07:11:19ZTeamwork: improved eQTL mapping using combinations of machine learning methods.1932-620310.1371/journal.pone.0040916https://doaj.org/article/0a71e1d33a424c78b7914b17a5b41f992012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22911718/?tool=EBIhttps://doaj.org/toc/1932-6203Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.Marit AckermannMathieu Clément-ZizaJacob J MichaelsonAndreas BeyerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 7, p e40916 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marit Ackermann Mathieu Clément-Ziza Jacob J Michaelson Andreas Beyer Teamwork: improved eQTL mapping using combinations of machine learning methods.
description	Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.
format	article
author	Marit Ackermann Mathieu Clément-Ziza Jacob J Michaelson Andreas Beyer
author_facet	Marit Ackermann Mathieu Clément-Ziza Jacob J Michaelson Andreas Beyer
author_sort	Marit Ackermann
title	Teamwork: improved eQTL mapping using combinations of machine learning methods.
title_short	Teamwork: improved eQTL mapping using combinations of machine learning methods.
title_full	Teamwork: improved eQTL mapping using combinations of machine learning methods.
title_fullStr	Teamwork: improved eQTL mapping using combinations of machine learning methods.
title_full_unstemmed	Teamwork: improved eQTL mapping using combinations of machine learning methods.
title_sort	teamwork: improved eqtl mapping using combinations of machine learning methods.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/0a71e1d33a424c78b7914b17a5b41f99
work_keys_str_mv	AT maritackermann teamworkimprovedeqtlmappingusingcombinationsofmachinelearningmethods AT mathieuclementziza teamworkimprovedeqtlmappingusingcombinationsofmachinelearningmethods AT jacobjmichaelson teamworkimprovedeqtlmappingusingcombinationsofmachinelearningmethods AT andreasbeyer teamworkimprovedeqtlmappingusingcombinationsofmachinelearningmethods
_version_	1718423770128973824

Teamwork: improved eQTL mapping using combinations of machine learning methods.

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