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Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification

Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering effective solutions and decisions. The latest dev...

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Autores principales: Hilal, Anwer Mustafa, Malibari, Areej A., Obayya, Marwa, Alzahrani, Jaber S., Alamgeer, Mohammad, Mohamed, Abdullah, Motwakel, Abdelwahed, Yaseen, Ishfaq, Hamza, Manar Ahmed, Zamani, Abu Sarwar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124108/
https://www.ncbi.nlm.nih.gov/pubmed/35607459
http://dx.doi.org/10.1155/2022/1698137
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author Hilal, Anwer Mustafa
Malibari, Areej A.
Obayya, Marwa
Alzahrani, Jaber S.
Alamgeer, Mohammad
Mohamed, Abdullah
Motwakel, Abdelwahed
Yaseen, Ishfaq
Hamza, Manar Ahmed
Zamani, Abu Sarwar
author_facet Hilal, Anwer Mustafa
Malibari, Areej A.
Obayya, Marwa
Alzahrani, Jaber S.
Alamgeer, Mohammad
Mohamed, Abdullah
Motwakel, Abdelwahed
Yaseen, Ishfaq
Hamza, Manar Ahmed
Zamani, Abu Sarwar
author_sort Hilal, Anwer Mustafa
collection PubMed
description Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering effective solutions and decisions. The latest developments of fuzzy systems with artificial intelligence techniques enable to design the effective microarray gene expression classification models. In this aspect, this study introduces a novel feature subset selection with optimal adaptive neuro-fuzzy inference system (FSS-OANFIS) for gene expression classification. The major aim of the FSS-OANFIS model is to detect and classify the gene expression data. To accomplish this, the FSS-OANFIS model designs an improved grey wolf optimizer-based feature selection (IGWO-FS) model to derive an optimal subset of features. Besides, the OANFIS model is employed for gene classification and the parameter tuning of the ANFIS model is adjusted by the use of coyote optimization algorithm (COA). The application of IGWO-FS and COA techniques helps in accomplishing enhanced microarray gene expression classification outcomes. The experimental validation of the FSS-OANFIS model has been performed using Leukemia, Prostate, DLBCL Stanford, and Colon Cancer datasets. The proposed FSS-OANFIS model has resulted in a maximum classification accuracy of 89.47%.
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spelling pubmed-91241082022-05-22 Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification Hilal, Anwer Mustafa Malibari, Areej A. Obayya, Marwa Alzahrani, Jaber S. Alamgeer, Mohammad Mohamed, Abdullah Motwakel, Abdelwahed Yaseen, Ishfaq Hamza, Manar Ahmed Zamani, Abu Sarwar Comput Intell Neurosci Research Article Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering effective solutions and decisions. The latest developments of fuzzy systems with artificial intelligence techniques enable to design the effective microarray gene expression classification models. In this aspect, this study introduces a novel feature subset selection with optimal adaptive neuro-fuzzy inference system (FSS-OANFIS) for gene expression classification. The major aim of the FSS-OANFIS model is to detect and classify the gene expression data. To accomplish this, the FSS-OANFIS model designs an improved grey wolf optimizer-based feature selection (IGWO-FS) model to derive an optimal subset of features. Besides, the OANFIS model is employed for gene classification and the parameter tuning of the ANFIS model is adjusted by the use of coyote optimization algorithm (COA). The application of IGWO-FS and COA techniques helps in accomplishing enhanced microarray gene expression classification outcomes. The experimental validation of the FSS-OANFIS model has been performed using Leukemia, Prostate, DLBCL Stanford, and Colon Cancer datasets. The proposed FSS-OANFIS model has resulted in a maximum classification accuracy of 89.47%. Hindawi 2022-05-14 /pmc/articles/PMC9124108/ /pubmed/35607459 http://dx.doi.org/10.1155/2022/1698137 Text en Copyright © 2022 Anwer Mustafa Hilal et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hilal, Anwer Mustafa
Malibari, Areej A.
Obayya, Marwa
Alzahrani, Jaber S.
Alamgeer, Mohammad
Mohamed, Abdullah
Motwakel, Abdelwahed
Yaseen, Ishfaq
Hamza, Manar Ahmed
Zamani, Abu Sarwar
Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification
title Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification
title_full Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification
title_fullStr Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification
title_full_unstemmed Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification
title_short Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification
title_sort feature subset selection with optimal adaptive neuro-fuzzy systems for bioinformatics gene expression classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124108/
https://www.ncbi.nlm.nih.gov/pubmed/35607459
http://dx.doi.org/10.1155/2022/1698137
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