AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine

Chaolu Meng; Shunshan Jin; Lei Wang; Fei Guo; Quan Zou

doi:10.3389/fbioe.2019.00224

AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine

Front Bioeng Biotechnol. 2019 Sep 18:7:224. doi: 10.3389/fbioe.2019.00224. eCollection 2019.

Authors

Chaolu Meng^{1

2}, Shunshan Jin³, Lei Wang⁴, Fei Guo¹, Quan Zou^{1

5

6}

Affiliations

¹ College of Intelligence and Computing, Tianjin University, Tianjin, China.
² College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
³ Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China.
⁴ College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.
⁵ Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
⁶ Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.

Abstract

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.

Keywords: antioxidant proteins; classifier; machine-learning; sequence features; support vector machine.