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

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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.
Variable selection, High-dimension, Regularization, Classification, Sparse-group Lasso
Bibliographic citation
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.