Improving eQTL Analysis Using a Machine Learning Approach for Data Integration

Detalhes bibliográficos
Autor(a) principal: Beretta, Stefano
Data de Publicação: 2018
Outros Autores: Castelli, Mauro, Gonçalves, Ivo, Kel, Ivan, Giansanti, Valentina, Merelli, Ivan
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/85459
Resumo: Beretta, S., Castelli, M., Gonçalves, I., Kel, I., Giansanti, V., & Merelli, I. (2018). Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution. Journal of Computational Biology, 25(10), 1091-1105. DOI: 10.1089/cmb.2017.0167
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spelling Improving eQTL Analysis Using a Machine Learning Approach for Data IntegrationA Logistic Model Tree Solutiondata integrationeQTL analysisevolutionary algorithmgenetic programmingmachine learningModelling and SimulationMolecular BiologyGeneticsComputational MathematicsComputational Theory and MathematicsBeretta, S., Castelli, M., Gonçalves, I., Kel, I., Giansanti, V., & Merelli, I. (2018). Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution. Journal of Computational Biology, 25(10), 1091-1105. DOI: 10.1089/cmb.2017.0167Expression quantitative trait loci (eQTL) analysis is an emerging method for establishing the impact of genetic variations (such as single nucleotide polymorphisms) on the expression levels of genes. Although different methods for evaluating the impact of these variations are proposed in the literature, the results obtained are mostly in disagreement, entailing a considerable number of false-positive predictions. For this reason, we propose an approach based on Logistic Model Trees that integrates the predictions of different eQTL mapping tools to produce more reliable results. More precisely, we employ a machine learning-based method using logistic functions to perform a linear regression able to classify the predictions of three eQTL analysis tools (namely, R/qtl, MatrixEQTL, and mRMR). Given the lack of a reference dataset and that computational predictions are not so easy to test experimentally, the performance of our approach is assessed using data from the DREAM5 challenge. The results show the quality of the aggregated prediction is better than that obtained by each single tool in terms of both precision and recall. We also performed a test on real data, employing genotypes and microRNA expression profiles from Caenorhabditis elegans, which proved that we were able to correctly classify all the experimentally validated eQTLs. These good results come both from the integration of the different predictions, and from the ability of this machine learning algorithm to find the best cutoff thresholds for each tool. This combination makes our integration approach suitable for improving eQTL predictions for testing in a laboratory, reducing the number of false-positive results.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBeretta, StefanoCastelli, MauroGonçalves, IvoKel, IvanGiansanti, ValentinaMerelli, Ivan2019-10-25T22:59:16Z2018-10-012018-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/85459eng1066-5277PURE: 6082339http://www.scopus.com/inward/record.url?scp=85054439017&partnerID=8YFLogxKhttps://doi.org/10.1089/cmb.2017.0167info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:41:54Zoai:run.unl.pt:10362/85459Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:13:20.582047Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
A Logistic Model Tree Solution
title Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
spellingShingle Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
Beretta, Stefano
data integration
eQTL analysis
evolutionary algorithm
genetic programming
machine learning
Modelling and Simulation
Molecular Biology
Genetics
Computational Mathematics
Computational Theory and Mathematics
title_short Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
title_full Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
title_fullStr Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
title_full_unstemmed Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
title_sort Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
author Beretta, Stefano
author_facet Beretta, Stefano
Castelli, Mauro
Gonçalves, Ivo
Kel, Ivan
Giansanti, Valentina
Merelli, Ivan
author_role author
author2 Castelli, Mauro
Gonçalves, Ivo
Kel, Ivan
Giansanti, Valentina
Merelli, Ivan
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Beretta, Stefano
Castelli, Mauro
Gonçalves, Ivo
Kel, Ivan
Giansanti, Valentina
Merelli, Ivan
dc.subject.por.fl_str_mv data integration
eQTL analysis
evolutionary algorithm
genetic programming
machine learning
Modelling and Simulation
Molecular Biology
Genetics
Computational Mathematics
Computational Theory and Mathematics
topic data integration
eQTL analysis
evolutionary algorithm
genetic programming
machine learning
Modelling and Simulation
Molecular Biology
Genetics
Computational Mathematics
Computational Theory and Mathematics
description Beretta, S., Castelli, M., Gonçalves, I., Kel, I., Giansanti, V., & Merelli, I. (2018). Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution. Journal of Computational Biology, 25(10), 1091-1105. DOI: 10.1089/cmb.2017.0167
publishDate 2018
dc.date.none.fl_str_mv 2018-10-01
2018-10-01T00:00:00Z
2019-10-25T22:59:16Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/85459
url http://hdl.handle.net/10362/85459
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1066-5277
PURE: 6082339
http://www.scopus.com/inward/record.url?scp=85054439017&partnerID=8YFLogxK
https://doi.org/10.1089/cmb.2017.0167
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eu_rights_str_mv openAccess
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application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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