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

Overview

Journal Front Bioeng Biotechnol

Date 2019 Oct 18

PMID 31620433

Citations 34

Authors

Chaolu Meng

Shunshan Jin

Lei Wang

Fei Guo

Quan Zou

Affiliations

Soon will be listed here.

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.

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