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

Juan C. Laria, M. Carmen Aguilera-Morillo, Enrique Álvarez, Rosa E. Lillo, Sara López-Taruella, María del Monte-Millán, Antonio C. Picornell, Miguel Martín and Juan Romo
Additional contact information
Juan C. Laria: UC3M-BS Santander Big Data Institute, 28903 Getafe, Spain
M. Carmen Aguilera-Morillo: UC3M-BS Santander Big Data Institute, 28903 Getafe, Spain
Enrique Álvarez: Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
Rosa E. Lillo: UC3M-BS Santander Big Data Institute, 28903 Getafe, Spain
Sara López-Taruella: Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
María del Monte-Millán: Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
Antonio C. Picornell: Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
Miguel Martín: Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
Juan Romo: UC3M-BS Santander Big Data Institute, 28903 Getafe, Spain

Mathematics, 2021, vol. 9, issue 3, 1-14

Abstract: 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.

Keywords: variable selection; high dimension; regularization; classification; sparse-group lasso (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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