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Table 5 Comparison of SUMOsu with other predictors.

From: Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder

Method Threshold Acc Sp Sn MCC
SUMOsp2.0 Low 0.83 0.83 0.75 0.30
SUMOhydro   0.91 0.91 0.71 0.41
seeSUMO-RF   0.82 0.83 0.75 0.30
seeSUMO-SVM   0.90 0.91 0.67 0.37
SUMOsu   0.96 0.97 0.67 0.56
SUMOsp2.0 Medium 0.91 0.93 0.63 0.38
SUMOhydro   0.92 0.94 0.67 0.43
seeSUMO-RF   0.88 0.88 0.71 0.35
seeSUMO-SVM   0.93 0.95 0.54 0.40
SUMOsu   0.96 0.97 0.58 0.52
SUMOsp2.0 High 0.95 0.96 0.58 0.47
SUMOhydro   0.93 0.95 0.58 0.42
seeSUMO-RF   0.89 0.90 0.67 0.36
seeSUMO-SVM   0.95 0.98 0.38 0.39
SUMOsu   0.96 0.98 0.58 0.54
Regular Expressions N/A 0.95 0.96 0.71 0.56
  1. The values of accuracy (Acc), specificity (Sp), sensitivity (Sn), and Matthew's correlation efficients (MCC) are obtained from Chen et al. [23] as the exact same independent dataset was employed in this study. Thresholds for SUMOsu was set as -0.5, 0, and 0.5 for low, medium and high, respectively.