ROC Curves for the Statistical Analysis of Microarray Data

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.endPage253es_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicaciónes_ES
UDC.journalTitleMicroarray Bioinformaticses_ES
UDC.startPage245es_ES
dc.contributor.authorCao, Ricardo
dc.contributor.authorLópez-de-Ullibarri, Ignacio
dc.date.accessioned2025-01-23T14:28:11Z
dc.date.available2025-01-23T14:28:11Z
dc.date.issued2019
dc.descriptionThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-1-4939-9442-7_11es_ES
dc.description.abstract[Abstract]: A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as a function of its discrimination threshold. This chapter is an overview on the use of ROC curves for microarray data. The notion of ROC curve and its motivation is introduced in Subheading 1. Relevant scientific contributions concerning the use of ROC curves for microarray data are briefly reviewed in Subheading 2. The special case with covariates is considered in Subheading 3. Two relevant aspects are reviewed in this section: the use of LASSO techniques for selecting and combining relevant markers and how to correct for multiple testing when a large number of markers are available. Finally, some conclusions are included.es_ES
dc.identifier.citationCao, R., López-de-Ullibarri, I. (2019). ROC Curves for the Statistical Analysis of Microarray Data. In: Bolón-Canedo, V., Alonso-Betanzos, A. (eds) Microarray Bioinformatics. Methods in Molecular Biology, vol 1986. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9442-7_11es_ES
dc.identifier.doi10.1007/978-1-4939-9442-7_11
dc.identifier.issn1064-3745
dc.identifier.urihttp://hdl.handle.net/2183/40877
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.relation.ispartofseriesMethods in Molecular Biology; 1986es_ES
dc.relation.urihttps://doi.org/10.1007/978-1-4939-9442-7_11es_ES
dc.rights© 2019 Springer Science+Business Media, LLC, part of Springer Naturees_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectAUCes_ES
dc.subjectFDRes_ES
dc.subjectFWERes_ES
dc.subjectLASSOes_ES
dc.subjectMicroarrayes_ES
dc.subjectMultiple testinges_ES
dc.subjectpAUCes_ES
dc.subjectROC curvees_ES
dc.titleROC Curves for the Statistical Analysis of Microarray Dataes_ES
dc.typebook partes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication3360aaca-39be-43b4-a458-974e79cdbc6b
relation.isAuthorOfPublication8fd68274-8739-496b-90df-2e0c821adae2
relation.isAuthorOfPublication.latestForDiscovery3360aaca-39be-43b4-a458-974e79cdbc6b

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