Table 2.
Comparison of performances of our model with existing method on benchmark dataset evaluated using cross-validation technique.
| Methods | Sensitivity | Specificity | Accuracy | AUROC | MCC |
|---|---|---|---|---|---|
| Sigma70Pred | 97.44 | 97.36 | 97.38 | 0.996 | 0.943 |
| iPro70-FMWin | 83.81 | 95.07 | 91.17 | 0.960 | 0.803 |
| 70ProPred* | 92.40 | 96.90 | 95.30 | 0.990 | 0.897 |
| iPro70-PseZNC* | 80.30 | 86.80 | 84.50 | 0.909 | 0.663 |
| Z-Curve* | 74.60 | 79.50 | 77.80 | 0.848 | 0.527 |
| IPMD* | 82.40 | 90.70 | 87.90 | – | 0.731 |
| iProEP | 89.52 | 64.03 | 76.88 | 0.654 | 0.554 |
*
Reported by the authors in the manuscript. The values in the tables are in bold to represent the best performing classifier or method.