AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine
AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine
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AOPs-SVM：使用支持向量机的基于序列的抗氧化蛋白分类器
DOI：
10.3389/fbioe.2019.00224
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发表时间：
2019-09-18
影响因子：
5.7
通讯作者：
Zou, Quan
中科院分区：
文献类型：
--
作者：
Meng, Chaolu;Jin, Shunshan;Zou, Quan
抗氧化蛋白在对抗生物体内的氧化损伤方面发挥着重要作用。由于其耗时长、成本高，利用生物实验准确鉴定抗氧化蛋白质是一项具有挑战性的任务。基于这些原因，我们提出了一种基于序列特征和支持向量机的基于机器学习算法的AOPS-SVM模型。使用测试数据集，我们与所提出的AOPS-支持向量机分类器进行了刀切交叉验证测试，获得了0.68%的灵敏度、0.985的特异度、0.942的平均准确率、0.741的MCC和0.832的AUC。这比现有的分类器表现更好。实验结果表明，AOPS-支持向量机是一种有效的分类器，有助于抗氧化蛋白质的相关研究。在http://server.malab.cn/AOPs-SVM/index.jsp建立了一台网络服务器，以提供开放访问。
Antioxidant proteins play important roles in countering oxidative damage in organisms. Because it is time-consuming and has a high cost, the accurate identification of antioxidant proteins using biological experiments is a challenging task. For these reasons, we proposed a model using machine-learning algorithms that we named AOPs-SVM, which was developed based on sequence features and a support vector machine. Using a testing dataset, we conducted a jackknife cross-validation test with the proposed AOPs-SVM classifier and obtained 0.68 in sensitivity, 0.985 in specificity, 0.942 in average accuracy, 0.741 in MCC, and 0.832 in AUC. This outperformed existing classifiers. The experiment results demonstrate that the AOPs-SVM is an effective classifier and contributes to the research related to antioxidant proteins. A web server was built at http://server.malab.cn/AOPs-SVM/index.jsp to provide open access.