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Adaptive group-regularized logistic elastic net regression

In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) [Formula: see text]-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance cla...

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Autores principales: Münch, Magnus M, Peeters, Carel F W, Van Der Vaart, Aad W, Van De Wiel, Mark A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596493/
https://www.ncbi.nlm.nih.gov/pubmed/31886488
http://dx.doi.org/10.1093/biostatistics/kxz062
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author Münch, Magnus M
Peeters, Carel F W
Van Der Vaart, Aad W
Van De Wiel, Mark A
author_facet Münch, Magnus M
Peeters, Carel F W
Van Der Vaart, Aad W
Van De Wiel, Mark A
author_sort Münch, Magnus M
collection PubMed
description In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) [Formula: see text]-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical–variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced.
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spelling pubmed-85964932021-11-18 Adaptive group-regularized logistic elastic net regression Münch, Magnus M Peeters, Carel F W Van Der Vaart, Aad W Van De Wiel, Mark A Biostatistics Articles In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) [Formula: see text]-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical–variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced. Oxford University Press 2019-12-30 /pmc/articles/PMC8596493/ /pubmed/31886488 http://dx.doi.org/10.1093/biostatistics/kxz062 Text en © The Author 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Münch, Magnus M
Peeters, Carel F W
Van Der Vaart, Aad W
Van De Wiel, Mark A
Adaptive group-regularized logistic elastic net regression
title Adaptive group-regularized logistic elastic net regression
title_full Adaptive group-regularized logistic elastic net regression
title_fullStr Adaptive group-regularized logistic elastic net regression
title_full_unstemmed Adaptive group-regularized logistic elastic net regression
title_short Adaptive group-regularized logistic elastic net regression
title_sort adaptive group-regularized logistic elastic net regression
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596493/
https://www.ncbi.nlm.nih.gov/pubmed/31886488
http://dx.doi.org/10.1093/biostatistics/kxz062
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