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 | Â | 44 | 171 | 18 | 19 | 69.84% | 90.47% | 85.32% | 80.15% | 0.61 |
MiPred | Â | 41 | 175 | 14 | 22 | 65.07% | 92.59% | 85.71% | 78.83% | 0.60 |
miPred | Â | 42 | 177 | 12 | 21 | 66.66% | 93.65% | 86.90% | 80.16% | 0.64 |
miR-KDE | 63/189 | 39 | 176 | 13 | 24 | 61.90% | 93.18% | 85.31% | 77.51% | 0.59 |
microPred | Â | 48 | 159 | 30 | 15 | 76.54% | 84.12% | 82.14% | 80.16% | 0.56 |
MiRenSVM | Â | 45 | 161 | 28 | 18 | 71.45% | 85.21% | 81.75% | 78.30% | 0.54 |
miR-BAG | Â | 46 | 166 | 23 | 17 | 73.01% | 87.83% | 84.13% | 80.42% | 0.59 |
Our approach | Â | 50 | 164 | 25 | 13 | 79.36% | 86.77% | 84.92% | 83.06% | 0.63 |