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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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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. |
format | Online Article Text |
id | pubmed-3963368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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