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Improving eQTL Analysis Using a Machine Learning Approach for Data Integration

2018-10-01Artigo científico Acesso aberto
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dc.contributor.authorBeretta, Stefano
dc.contributor.authorCastelli, Mauro
dc.contributor.authorGonçalves, Ivo
dc.contributor.authorKel, Ivan
dc.contributor.authorGiansanti, Valentina
dc.contributor.authorMerelli, Ivan
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblMary Ann Leibert
dc.date.accessioned2019-10-25T22:59:16Z
dc.date.available2019-10-25T22:59:16Z
dc.date.issued2018-10-01
dc.descriptionBeretta, 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
dc.description.abstractExpression 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.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent15
dc.format.extent589626
dc.identifier.doi10.1089/cmb.2017.0167
dc.identifier.issn1066-5277
dc.identifier.otherPURE: 6082339
dc.identifier.otherPURE UUID: d0214b72-4f7c-41ca-9579-a8b4b0b45b9c
dc.identifier.otherScopus: 85054439017
dc.identifier.otherPubMed: 30052049
dc.identifier.otherWOS: 000440142400001
dc.identifier.otherORCID: /0000-0002-8793-1451/work/72856166
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85054439017&partnerID=8YFLogxK
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000440142400001
dc.identifier.urihttp://hdl.handle.net/10362/85459
dc.identifier.urlhttps://www.scopus.com/pages/publications/85054439017
dc.identifier.urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000440142400001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectdata integration
dc.subjecteQTL analysis
dc.subjectevolutionary algorithm
dc.subjectgenetic programming
dc.subjectmachine learning
dc.subjectModelling and Simulation
dc.subjectMolecular Biology
dc.subjectGenetics
dc.subjectComputational Mathematics
dc.subjectComputational Theory and Mathematics
dc.titleImproving eQTL Analysis Using a Machine Learning Approach for Data Integrationen
dc.title.subtitleA Logistic Model Tree Solutionen
dc.typejournal article
degois.publication.firstPage1091
degois.publication.issue10
degois.publication.lastPage1105
degois.publication.titleJournal of Computational Biology
degois.publication.volume25
dspace.entity.typePublication
rcaap.rightsopenAccess

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