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Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data

Classification and selection of gene in high dimensional microarray data has become a challenging problem in molecular biology and genetics. Penalized Adaptive likelihood method has been employed recently for classification of cancer to address both gene selection consistency and estimation of gene...

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Main Authors: Kargi, Isah Aliyu, Ismail, Norazlina, Mohamad, Ismail
Format: Article
Language:English
Published: International Association of Online Engineering 2021
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/97423/1/NorazlinaIsmail2021_ImprovedLassoIlassoForGeneSelection.pdf
http://eprints.utm.my/id/eprint/97423/
http://dx.doi.org/10.3991/ijoe.v17i08.24601
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spelling my.utm.974232022-10-10T07:31:05Z http://eprints.utm.my/id/eprint/97423/ Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data Kargi, Isah Aliyu Ismail, Norazlina Mohamad, Ismail QA Mathematics Classification and selection of gene in high dimensional microarray data has become a challenging problem in molecular biology and genetics. Penalized Adaptive likelihood method has been employed recently for classification of cancer to address both gene selection consistency and estimation of gene coefficients in high dimensional data simultaneously. Many studies from the literature have proposed the use of ordinary least squares (OLS), maximum likelihood estimation (MLE) and Elastic net as the initial weight in the Adaptive elastic net, but in high dimensional microarray data the MLE and OLS are not suitable. Likewise, considering the Elastic net as the initial weight in Adaptive elastic yields a poor performance, because the ridge penalty in the Elastic net grouped coefficient of highly correlated genes closer to each other. As a result, the estimator fails to differentiate coefficients of highly correlated genes that have different sign being grouped together. To tackle this issue, the present study proposed Improved LASSO (ILASSO) estimator which add the ridge penalty to the original LASSO with an Adaptive weight to both l1 - norm and l2 - norm simultaneously. Results from the real data indicated that ILASSO has a better performance compared to other methods in terms of the number of gene selected, classification precision, Sensitivity and Specificity. International Association of Online Engineering 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/97423/1/NorazlinaIsmail2021_ImprovedLassoIlassoForGeneSelection.pdf Kargi, Isah Aliyu and Ismail, Norazlina and Mohamad, Ismail (2021) Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data. International journal of online and biomedical engineering, 17 (8). pp. 91-102. ISSN 2626-8493 http://dx.doi.org/10.3991/ijoe.v17i08.24601 DOI : 10.3991/ijoe.v17i08.24601
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/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Kargi, Isah Aliyu
Ismail, Norazlina
Mohamad, Ismail
Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data
description Classification and selection of gene in high dimensional microarray data has become a challenging problem in molecular biology and genetics. Penalized Adaptive likelihood method has been employed recently for classification of cancer to address both gene selection consistency and estimation of gene coefficients in high dimensional data simultaneously. Many studies from the literature have proposed the use of ordinary least squares (OLS), maximum likelihood estimation (MLE) and Elastic net as the initial weight in the Adaptive elastic net, but in high dimensional microarray data the MLE and OLS are not suitable. Likewise, considering the Elastic net as the initial weight in Adaptive elastic yields a poor performance, because the ridge penalty in the Elastic net grouped coefficient of highly correlated genes closer to each other. As a result, the estimator fails to differentiate coefficients of highly correlated genes that have different sign being grouped together. To tackle this issue, the present study proposed Improved LASSO (ILASSO) estimator which add the ridge penalty to the original LASSO with an Adaptive weight to both l1 - norm and l2 - norm simultaneously. Results from the real data indicated that ILASSO has a better performance compared to other methods in terms of the number of gene selected, classification precision, Sensitivity and Specificity.
format Article
author Kargi, Isah Aliyu
Ismail, Norazlina
Mohamad, Ismail
author_facet Kargi, Isah Aliyu
Ismail, Norazlina
Mohamad, Ismail
author_sort Kargi, Isah Aliyu
title Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data
title_short Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data
title_full Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data
title_fullStr Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data
title_full_unstemmed Improved LASSO (ILASSO) for gene selection and classification in high dimensional dna microarray data
title_sort improved lasso (ilasso) for gene selection and classification in high dimensional dna microarray data
publisher International Association of Online Engineering
publishDate 2021
url http://eprints.utm.my/id/eprint/97423/1/NorazlinaIsmail2021_ImprovedLassoIlassoForGeneSelection.pdf
http://eprints.utm.my/id/eprint/97423/
http://dx.doi.org/10.3991/ijoe.v17i08.24601
_version_ 1748180457399255040
score 13.211869

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