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

Juan Carlos Laria de la Cruz, María del Carmen Aguilera Morillo, Enrique Álvarez, Rosa Elvira Lillo Rodríguez, Sara López Taruella, María Del Monte Millán, Antonio C. Picornell, Miguel Martín and Juan Romo

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: In the last decade, regularized regression methods have offered alternatives forperforming multi-marker analysis and feature selection in a whole genome context.The process of defining a list of genes that will characterize an expressionprofile, remains unclear. This procedure oscillates between selecting the genes or transcripts of interest based on previous clinical evidence, or performing a whole transcriptome analys is that rests on advanced statistics. 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.

Keywords: Variable; Selection; High-Dimension; Regularization; Classification (search for similar items in EconPapers)
Date: 2020-06-05
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