التفاصيل البيبلوغرافية
| العنوان: |
Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer. |
| المؤلفون: |
Laria, Juan C.1 (AUTHOR) jlaria@est-econ.uc3m.es, Aguilera-Morillo, M. Carmen1,2 (AUTHOR) lillo@est-econ.uc3m.es, Álvarez, Enrique3 (AUTHOR) enrique.alvarez@iisgm.com, Lillo, Rosa E.1,4 (AUTHOR) juan.romo@uc3m.es, López-Taruella, Sara3,5,6,7 (AUTHOR) sara.lopeztarruella@salud.madrid.org, del Monte-Millán, María3,5 (AUTHOR) maria.delmonte.externo@salud.madrid.org, Picornell, Antonio C.3 (AUTHOR) antonio.picornell@iisgm.com, Martín, Miguel3,5,6,7 (AUTHOR) mmartin@geicam.org, Romo, Juan1,4 (AUTHOR), De Asís Torres-Ruiz, Francisco (AUTHOR) |
| المصدر: |
Mathematics (2227-7390). Feb2021, Vol. 9 Issue 3, p222. 1p. |
| مصطلحات موضوعية: |
*TRIPLE-negative breast cancer, *BREAST cancer prognosis, *GENES, *FEATURE selection |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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