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 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

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