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Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review

Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled acc...

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Main Authors: Besar, Rosli, Mohd Ali, Nursabillilah, Ab. Aziz, Nor Azlina
Format: Article
Language:English
Published: MDPI 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26371/2/SYMMETRY-14-01955.PDF
http://eprints.utem.edu.my/id/eprint/26371/
https://www.mdpi.com/2073-8994/14/10/1955
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spelling my.utem.eprints.263712023-02-23T10:50:59Z http://eprints.utem.edu.my/id/eprint/26371/ Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review Besar, Rosli Mohd Ali, Nursabillilah Ab. Aziz, Nor Azlina Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. Nevertheless, the microarray datasets suffer from a large number of gene expression levels, limited sample size, and irrelevant features. Additionally, datasets are often asymmetrical, where the number of samples from different classes is not balanced. These limitations make it difficult to determine the actual features that contribute to the existence of cancer classification in the gene expression profiles. Various accurate feature selection methods exist, and they are being widely applied. The objective of feature selection is to search for a relevant, discriminant feature subset from the basic feature space. In this review, we aim to compile and review the latest hybrid feature selection methods based on bio-inspired metaheuristic methods and wrapper methods for the classification of BC and other types of cancer. MDPI 2022-09-20 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26371/2/SYMMETRY-14-01955.PDF Besar, Rosli and Mohd Ali, Nursabillilah and Ab. Aziz, Nor Azlina (2022) Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review. Symmetry, 14 (10). pp. 1-36. ISSN 2073-8994 https://www.mdpi.com/2073-8994/14/10/1955 10.3390/sym14101955
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. Nevertheless, the microarray datasets suffer from a large number of gene expression levels, limited sample size, and irrelevant features. Additionally, datasets are often asymmetrical, where the number of samples from different classes is not balanced. These limitations make it difficult to determine the actual features that contribute to the existence of cancer classification in the gene expression profiles. Various accurate feature selection methods exist, and they are being widely applied. The objective of feature selection is to search for a relevant, discriminant feature subset from the basic feature space. In this review, we aim to compile and review the latest hybrid feature selection methods based on bio-inspired metaheuristic methods and wrapper methods for the classification of BC and other types of cancer.
format Article
author Besar, Rosli
Mohd Ali, Nursabillilah
Ab. Aziz, Nor Azlina
spellingShingle Besar, Rosli
Mohd Ali, Nursabillilah
Ab. Aziz, Nor Azlina
Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review
author_facet Besar, Rosli
Mohd Ali, Nursabillilah
Ab. Aziz, Nor Azlina
author_sort Besar, Rosli
title Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review
title_short Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review
title_full Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review
title_fullStr Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review
title_full_unstemmed Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review
title_sort hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review
publisher MDPI
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26371/2/SYMMETRY-14-01955.PDF
http://eprints.utem.edu.my/id/eprint/26371/
https://www.mdpi.com/2073-8994/14/10/1955
_version_ 1758953856412155904
score 13.211869

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