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SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant p...

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Detalles Bibliográficos
Autores principales: Xu, Lei, Liang, Guangmin, Shi, Shuhua, Liao, Changrui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6032279/
https://www.ncbi.nlm.nih.gov/pubmed/29914044
http://dx.doi.org/10.3390/ijms19061773
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author Xu, Lei
Liang, Guangmin
Shi, Shuhua
Liao, Changrui
author_facet Xu, Lei
Liang, Guangmin
Shi, Shuhua
Liao, Changrui
author_sort Xu, Lei
collection PubMed
description Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.
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spelling pubmed-60322792018-07-13 SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins Xu, Lei Liang, Guangmin Shi, Shuhua Liao, Changrui Int J Mol Sci Article Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques. MDPI 2018-06-15 /pmc/articles/PMC6032279/ /pubmed/29914044 http://dx.doi.org/10.3390/ijms19061773 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Lei
Liang, Guangmin
Shi, Shuhua
Liao, Changrui
SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
title SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
title_full SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
title_fullStr SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
title_full_unstemmed SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
title_short SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
title_sort seqsvm: a sequence-based support vector machine method for identifying antioxidant proteins
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6032279/
https://www.ncbi.nlm.nih.gov/pubmed/29914044
http://dx.doi.org/10.3390/ijms19061773
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