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Identification of Antioxidants from Sequence Information Using Naïve Bayes

Antioxidant proteins are substances that protect cells from the damage caused by free radicals. Accurate identification of new antioxidant proteins is important in understanding their roles in delaying aging. Therefore, it is highly desirable to develop computational methods to identify antioxidant...

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Detalles Bibliográficos
Autores principales: Feng, Peng-Mian, Lin, Hao, Chen, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766563/
https://www.ncbi.nlm.nih.gov/pubmed/24062796
http://dx.doi.org/10.1155/2013/567529
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author Feng, Peng-Mian
Lin, Hao
Chen, Wei
author_facet Feng, Peng-Mian
Lin, Hao
Chen, Wei
author_sort Feng, Peng-Mian
collection PubMed
description Antioxidant proteins are substances that protect cells from the damage caused by free radicals. Accurate identification of new antioxidant proteins is important in understanding their roles in delaying aging. Therefore, it is highly desirable to develop computational methods to identify antioxidant proteins. In this study, a Naïve Bayes-based method was proposed to predict antioxidant proteins using amino acid compositions and dipeptide compositions. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife test, the proposed method achieved an accuracy of 66.88% for the discrimination between antioxidant and nonantioxidant proteins, which is superior to that of other state-of-the-art classifiers. These results suggest that the proposed method could be an effective and promising high-throughput method for antioxidant protein identification.
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spelling pubmed-37665632013-09-23 Identification of Antioxidants from Sequence Information Using Naïve Bayes Feng, Peng-Mian Lin, Hao Chen, Wei Comput Math Methods Med Research Article Antioxidant proteins are substances that protect cells from the damage caused by free radicals. Accurate identification of new antioxidant proteins is important in understanding their roles in delaying aging. Therefore, it is highly desirable to develop computational methods to identify antioxidant proteins. In this study, a Naïve Bayes-based method was proposed to predict antioxidant proteins using amino acid compositions and dipeptide compositions. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife test, the proposed method achieved an accuracy of 66.88% for the discrimination between antioxidant and nonantioxidant proteins, which is superior to that of other state-of-the-art classifiers. These results suggest that the proposed method could be an effective and promising high-throughput method for antioxidant protein identification. Hindawi Publishing Corporation 2013 2013-08-24 /pmc/articles/PMC3766563/ /pubmed/24062796 http://dx.doi.org/10.1155/2013/567529 Text en Copyright © 2013 Peng-Mian Feng 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 Research Article
Feng, Peng-Mian
Lin, Hao
Chen, Wei
Identification of Antioxidants from Sequence Information Using Naïve Bayes
title Identification of Antioxidants from Sequence Information Using Naïve Bayes
title_full Identification of Antioxidants from Sequence Information Using Naïve Bayes
title_fullStr Identification of Antioxidants from Sequence Information Using Naïve Bayes
title_full_unstemmed Identification of Antioxidants from Sequence Information Using Naïve Bayes
title_short Identification of Antioxidants from Sequence Information Using Naïve Bayes
title_sort identification of antioxidants from sequence information using naïve bayes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766563/
https://www.ncbi.nlm.nih.gov/pubmed/24062796
http://dx.doi.org/10.1155/2013/567529
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