AOPs-SVM: Sequence-based Classifier of Antioxidant Proteins Using a Support Vector Machine
About
Antioxidant Proteins play important roles in countering oxidative damage in organisms. Because of time-consuming and
highly cost, identifying the Antioxidant Proteins accurately using biological experiment is a challenge task.
For these reason, we proposed a model using machine learning algorithms, namedAOPs-SVM , which was developed
based on sequence features and support vector machine. The proposed classifier AOPs-SVM obtained 0.68 in sensitivity,
0.985 in specificity, 0.942 in average accuracy, 0.741 in MCC and 0.832 in AUC respectively in the testing dataset via
jackknife cross-validation test. It outperformed existing classifiers. The experimental results demonstrate that theAOPs-SVM
is an effective classifier and also contributes to the Antioxidant proteins related research.