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

• 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

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.