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A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with con...

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Detalles Bibliográficos
Autores principales: Li, Shan, Kang, Liying, Zhao, Xing-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3963368/
https://www.ncbi.nlm.nih.gov/pubmed/24729969
http://dx.doi.org/10.1155/2014/362738
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author Li, Shan
Kang, Liying
Zhao, Xing-Ming
author_facet Li, Shan
Kang, Liying
Zhao, Xing-Ming
author_sort Li, Shan
collection PubMed
description With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.
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spelling pubmed-39633682014-04-13 A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics Li, Shan Kang, Liying Zhao, Xing-Ming Biomed Res Int Review Article With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. Hindawi Publishing Corporation 2014 2014-03-06 /pmc/articles/PMC3963368/ /pubmed/24729969 http://dx.doi.org/10.1155/2014/362738 Text en Copyright © 2014 Shan Li et al. https://creativecommons.org/licenses/by/3.0/ 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 Review Article
Li, Shan
Kang, Liying
Zhao, Xing-Ming
A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
title A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
title_full A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
title_fullStr A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
title_full_unstemmed A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
title_short A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
title_sort survey on evolutionary algorithm based hybrid intelligence in bioinformatics
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3963368/
https://www.ncbi.nlm.nih.gov/pubmed/24729969
http://dx.doi.org/10.1155/2014/362738
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