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

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Show simple item record Laria de la Cruz, Juan Carlos Aguilera Morillo, María del Carmen Álvarez Castillo, Enrique Luis Lillo Rodríguez, Rosa Elvira López Taruella, Sara Del Monte Millán, María Picornell, Antonio C. Martín, Miguel Romo, Juan 2021-03-18T12:14:34Z 2021-03-18T12:14:34Z 2021-01-23
dc.identifier.bibliographicCitation Laria, 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.issn 2227-7390
dc.description.abstract Over 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.
dc.description.sponsorship This work was partially supported by a research grant from Instituto de Salud Carlos III (PI 15/00117), co-funded by FEDER, to M. Martín.
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2021 by the authors
dc.rights Atribución 3.0 España
dc.subject.other Variable selection
dc.subject.other High-dimension
dc.subject.other Regularization
dc.subject.other Classification
dc.subject.other Sparse-group Lasso
dc.title Iterative variable selection for high-dimensional data: Prediction of pathological response in triple-negative breast cancer
dc.type article
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 14
dc.identifier.publicationtitle Mathematics
dc.identifier.publicationvolume 9
dc.identifier.uxxi AR/0000027093
dc.affiliation.instituto UC3M. Instituto UC3M - Santander de Big Data
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