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dc.contributor.authorUrda, Daniel
dc.contributor.authorLuque-Baena, Rafael
dc.contributor.authorFranco, Leonardo 
dc.contributor.authorSánchez-Maroño, Noelia
dc.contributor.authorJerez-Aragonés, José Manuel 
dc.date.accessioned2017-06-26T11:27:10Z
dc.date.available2017-06-26T11:27:10Z
dc.date.created2017
dc.date.issued2017-06-26
dc.identifier.urihttp://hdl.handle.net/10630/14000
dc.description.abstractClinicians are interested in the estimation of robust and relevant genetic signatures from gene sequencing data. Many machine learning approaches have been proposed trying to address well-known issues of this complex task (feature or gene selection, classification or model selection, and prediction assessment). Addressing this problem often requires a deep knowledge of these methods and some of them demand high computational resources that may not be affordable. In this paper, an exhaustive study that includes different types of feature selection methods and classifiers is presented, providing clinicians an useful insight of the most suitable methods for this purpose. Predictions assessment is performed using a bootstrap crossvalidation strategy as an honest validation scheme. The results of this study for six benchmark datasets show that filter or embedded methods are preferred, in general, to wrapper methods according to their better statistical significant results, in terms of accuracy, and lower demand for computational resources.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectBioinformáticaes_ES
dc.subjectBiología computacionales_ES
dc.subject.otherBioinformaticses_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherGenetic signatureses_ES
dc.titleMachine learning models to search relevant genetic signatures in clinical contextes_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleInternational Joint Conference on Neural Networks (2017)es_ES
dc.relation.eventplaceAnchorage (USA)es_ES
dc.relation.eventdate14/05/2017es_ES
dc.identifier.orcidhttp://orcid.org/0000-0002-7858-2966es_ES
dc.cclicenseby-nc-ndes_ES


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