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Open AccessArticle

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

1
School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China
2
Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(6), 1773; https://doi.org/10.3390/ijms19061773
Received: 21 May 2018 / Revised: 10 June 2018 / Accepted: 11 June 2018 / Published: 15 June 2018
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2018)
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. View Full-Text
Keywords: antioxidant protein; primary sequence; support vector machine; maximum relevance maximum distance; feature selection antioxidant protein; primary sequence; support vector machine; maximum relevance maximum distance; feature selection
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MDPI and ACS Style

Xu, L.; Liang, G.; Shi, S.; Liao, C. SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins. Int. J. Mol. Sci. 2018, 19, 1773.

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