Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer

Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer

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...

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Bibliographic Details
Main Authors: Juan C. Laria, M. Carmen Aguilera-Morillo, Enrique Álvarez, Rosa E. Lillo, Sara López-Taruella, María del Monte-Millán, Antonio C. Picornell, Miguel Martín, Juan Romo
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Mathematics
Subjects:
variable selection
high dimension
regularization
classification
sparse-group lasso
Online Access:https://www.mdpi.com/2227-7390/9/3/222

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