Table 8 Performance comparison with previous studies using newly released data from miRBase.
From: ViralmiR: a support-vector-machine-based method for predicting viral microRNA precursors
| Tool | Positive dataset/negative dataset | TP | TN | FP | FN | SN | SP | ACC | Balanced ACC | MCC |
|---|---|---|---|---|---|---|---|---|---|---|
| Triplet-SVM | 22 | 88 | 8 | 10 | 68.75% | 91.67% | 85.94% | 80.21% | 0.62 | |
| MiPred | 20 | 79 | 17 | 12 | 62.50% | 82.29% | 77.34% | 72.40% | 0.43 | |
| miPred | 24 | 85 | 11 | 8 | 75.00% | 88.54% | 85.16% | 81.77% | 0.62 | |
| miR-KDE | 32/96 | 23 | 81 | 15 | 9 | 71.88% | 84.38% | 81.25% | 78.13% | 0.53 |
| microPred | 23 | 86 | 10 | 9 | 71.88% | 89.58% | 85.16% | 80.73% | 0.61 | |
| MiRenSVM | 19 | 81 | 15 | 13 | 59.38% | 84.38% | 78.13% | 71.88% | 0.43 | |
| miR-BAG | 22 | 82 | 14 | 10 | 68.75% | 85.42% | 81.25% | 77.08% | 0.52 | |
| ViralmiR | 25 | 85 | 11 | 7 | 78.13% | 88.54% | 85.94% | 83.33% | 0.64 |