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Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification

Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in h...

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Main Authors: Algamal, Zakariya Y., Lee, Muhammad Hisyam
Format: Article
Published: Elsevier Limited 2015
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QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/55241/
http://dx.doi.org/10.1016/j.compbiomed.2015.10.008
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spelling my.utm.552412016-09-04T01:24:08Z http://eprints.utm.my/id/eprint/55241/ Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification Algamal, Zakariya Y. Lee, Muhammad Hisyam QA Mathematics Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification. Elsevier Limited 2015-12-01 Article PeerReviewed Algamal, Zakariya Y. and Lee, Muhammad Hisyam (2015) Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification. Computers in Biology and Medicine, 67 . pp. 136-145. ISSN 0010-4825 http://dx.doi.org/10.1016/j.compbiomed.2015.10.008 DOI:10.1016/j.compbiomed.2015.10.008
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Algamal, Zakariya Y.
Lee, Muhammad Hisyam
Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
description Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.
format Article
author Algamal, Zakariya Y.
Lee, Muhammad Hisyam
author_facet Algamal, Zakariya Y.
Lee, Muhammad Hisyam
author_sort Algamal, Zakariya Y.
title Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
title_short Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
title_full Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
title_fullStr Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
title_full_unstemmed Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
title_sort regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
publisher Elsevier Limited
publishDate 2015
url http://eprints.utm.my/id/eprint/55241/
http://dx.doi.org/10.1016/j.compbiomed.2015.10.008
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score 13.211869

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