Table 3.
Results of 10-fold cross validation of SVM model on different combinations of the three features we used in our method.
| F1 and F2 | F3 | Sn (%) | Sp (%) | Acc (%) | PPV (%) | NPV (%) | MCC | AUC |
|---|---|---|---|---|---|---|---|---|
| SAR | 5 partitions | 88.24 | 97.34 | 92.79 | 97.07 | 89.22 | 0.859 | 0.963 |
| SAR | 7 partitions | 88.24 | 97.96 | 93.10 | 97.74 | 89.29 | 0.866 | 0.965 |
| SAR | 9 partitions | 89.19 | 96.08 | 92.63 | 95.79 | 89.88 | 0.855 | 0.962 |
| DAR | 5 partitions | 84.80 | 94.51 | 89.66 | 93.92 | 86.14 | 0.797 | 0.937 |
| DAR | 7 partitions | 85.42 | 94.51 | 89.97 | 93.97 | 86.64 | 0.803 | 0.938 |
| DAR | 9 partitions | 85.27 | 94.20 | 89.73 | 93.63 | 86.47 | 0.798 | 0.940 |
SAR: single amino acid repeats for F1 and F2, DAR: double amino acid repeats for F1 and F2, Sn: sensitivity, Sp: specificity, Acc: accuracy, PPV: positive predictive value, NPV: negative predictive value, MCC: Matthews correlation coefficient, and AUC: the area under the ROC curve.