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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6032279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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