School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China.
Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Int J Mol Sci. 2018 Jun 15;19(6):1773. doi: 10.3390/ijms19061773.
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.
抗氧化蛋白在疾病预防中可能有益。更多的关注已经集中在抗氧化蛋白的功能上。因此，鉴定抗氧化蛋白对于研究很重要。在我们的工作中，我们提出了一种基于其一级序列特征的预测抗氧化蛋白的计算方法，称为 SeqSVM。通过最大相关性最大距离方法消除特征以减少冗余。最后，通过支持向量机（SVM）识别抗氧化蛋白。实验结果表明，我们的方法比现有方法表现更好，整体准确率为 89.46%。尽管提出的计算方法可以达到令人鼓舞的分类结果，但实验结果是基于生化方法（如湿生化和分子生物学技术）验证的。
