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

Author

Listed:
  • 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
    Department of Applied Statistics and Operational Research and Quality, Universitat Politècnica de València, 46022 Valencia, 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
    Department of Statistics, University Carlos III of Madrid, 28903 Getafe, Spain)

  • Sara López-Taruella

    (Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
    CiberOnc—Centro de Investigación Biomédica en Red, 28029 Madrid, Spain
    Universidad Complutense de Madrid, 28040 Madrid, Spain
    GEICAM—Grupo Español de Investigación en Cáncer de Mama, 28703 San Sebastián de los Reyes, Spain)

  • María del Monte-Millán

    (Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
    CiberOnc—Centro de Investigación Biomédica en Red, 28029 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
    CiberOnc—Centro de Investigación Biomédica en Red, 28029 Madrid, Spain
    Universidad Complutense de Madrid, 28040 Madrid, Spain
    GEICAM—Grupo Español de Investigación en Cáncer de Mama, 28703 San Sebastián de los Reyes, Spain)

  • Juan Romo

    (UC3M-BS Santander Big Data Institute, 28903 Getafe, Spain
    Department of Statistics, University Carlos III of Madrid, 28903 Getafe, Spain)

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.

Suggested Citation

  • Juan C. Laria & M. Carmen Aguilera-Morillo & Enrique Álvarez & Rosa E. Lillo & Sara López-Taruella & María del Monte-Millán & Antonio C. Picornell & Miguel Martín & Juan Romo, 2021. "Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer," Mathematics, MDPI, vol. 9(3), pages 1-14, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:3:p:222-:d:485789
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    References listed on IDEAS

    as
    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    3. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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