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Iterative variable selection for high-dimensional data: Prediction of pathological response in triple-negative breast cancer

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dc.affiliation.institutoUC3M. Instituto UC3M - Santander de Big Dataes
dc.contributor.authorLaria de la Cruz, Juan Carlos
dc.contributor.authorAguilera Morillo, María del Carmen
dc.contributor.authorÁlvarez Castillo, Enrique Luis
dc.contributor.authorLillo Rodríguez, Rosa Elvira
dc.contributor.authorLópez Taruella, Sara
dc.contributor.authorDel Monte Millán, María
dc.contributor.authorPicornell, Antonio C.
dc.contributor.authorMartín, Miguel
dc.contributor.authorRomo, Juan
dc.date.accessioned2021-03-18T12:14:34Z
dc.date.available2021-03-18T12:14:34Z
dc.date.issued2021-01-23
dc.description.abstractOver the last decade, regularized regression methods have offered alternatives for performing multi-marker analysis and feature selection in a whole genome context. The process of defining a list of genes that will characterize an expression profile remains unclear. It currently relies upon advanced statistics and can use an agnostic point of view or include some a priori knowledge, but overfitting remains a problem. This paper introduces a methodology to deal with the variable selection and model estimation problems in the high-dimensional set-up, which can be particularly useful in the whole genome context. Results are validated using simulated data and a real dataset from a triple-negative breast cancer study.en
dc.description.sponsorshipThis work was partially supported by a research grant from Instituto de Salud Carlos III (PI 15/00117), co-funded by FEDER, to M. Martín.en
dc.identifier.bibliographicCitationLaria, J.C.; Aguilera-Morillo, M.C.; Álvarez, E.; Lillo, R.E.; López-Taruella, S.; del Monte-Millán, M.; Picornell, A.C.; Martín, M.; Romo, J. Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer. Mathematics 2021, 9, 222.
dc.identifier.doihttps://doi.org/10.3390/math9030222
dc.identifier.issn2227-7390
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue3es
dc.identifier.publicationlastpage14es
dc.identifier.publicationtitleMathematicses
dc.identifier.publicationvolume9es
dc.identifier.urihttps://hdl.handle.net/10016/32185
dc.identifier.uxxiAR/0000027093
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2021 by the authors
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subject.ecienciaEstadísticaes
dc.subject.otherVariable selectionen
dc.subject.otherHigh-dimensionen
dc.subject.otherRegularizationen
dc.subject.otherClassificationen
dc.subject.otherSparse-group Lassoen
dc.titleIterative variable selection for high-dimensional data: Prediction of pathological response in triple-negative breast canceren
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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