Skip to main content

Table 2 10-fold cross-validation with forty selected features using different machine learning methods on RSC758

From: Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features

  ACC SN SP MCC AUC
SVM 0.679 0.602 0.756 0.362 0.727
Naive Bayes 0.648 0.450 0.846 0.322 0.713
Random Forest 0.664 0.611 0.718 0.330 0.711
Artificial Neural Network 0.662 0.615 0.708 0.325 0.698
  1. The results are sorted by AUC value