RT Journal Article
T1 Iterative variable selection for high-dimensional data: Prediction of pathological response in triple-negative breast cancer
A1 Laria de la Cruz, Juan Carlos
A1 Aguilera Morillo, María del Carmen
A1 Álvarez Castillo, Enrique Luis
A1 Lillo Rodríguez, Rosa Elvira
A1 López Taruella, Sara
A1 Del Monte Millán, María
A1 Picornell, Antonio C.
A1 Martín, Miguel
A1 Romo, Juan
AB 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.
PB MDPI
SN 2227-7390
YR 2021
FD 2021-01-23
LK https://hdl.handle.net/10016/32185
UL https://hdl.handle.net/10016/32185
LA eng
NO 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.
DS e-Archivo
RD 27 jul. 2024