A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

Abstract Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been...

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Autores principales:	Yosef Masoudi-Sobhanzadeh, Habib Motieghader, Yadollah Omidi, Ali Masoudi-Nejad
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f42431c0668e4ac890baa5be3d0c88d6
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spelling	oai:doaj.org-article:f42431c0668e4ac890baa5be3d0c88d62021-12-02T13:30:28ZA machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications10.1038/s41598-021-82796-y2045-2322https://doaj.org/article/f42431c0668e4ac890baa5be3d0c88d62021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82796-yhttps://doaj.org/toc/2045-2322Abstract Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.Yosef Masoudi-SobhanzadehHabib MotieghaderYadollah OmidiAli Masoudi-NejadNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yosef Masoudi-Sobhanzadeh Habib Motieghader Yadollah Omidi Ali Masoudi-Nejad A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications
description	Abstract Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.
format	article
author	Yosef Masoudi-Sobhanzadeh Habib Motieghader Yadollah Omidi Ali Masoudi-Nejad
author_facet	Yosef Masoudi-Sobhanzadeh Habib Motieghader Yadollah Omidi Ali Masoudi-Nejad
author_sort	Yosef Masoudi-Sobhanzadeh
title	A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications
title_short	A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications
title_full	A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications
title_fullStr	A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications
title_full_unstemmed	A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications
title_sort	machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f42431c0668e4ac890baa5be3d0c88d6
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A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

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