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 list genes that will characterize an expression profile remains unclear. It currently relies upon advanced statistics can use agnostic point view or include some priori knowledge, but overfitting problem. This paper introduces methodology to deal with variable model estimation problems high-dimensional set-up, which be particularly useful Results are validated using simulated data real dataset from triple-negative breast cancer study.