Table 2 The self-consistency and jackknife results for the sequence-based method trained by the six combinations listed in table 1.
From: Demonstration of two novel methods for predicting functional siRNA efficiency
| Â | Dieter's Dataset | Satron's Dataset | ||
|---|---|---|---|---|
| Â | Self-Consistency | Jackknife | Self-consistency | Jackknife |
Cut-Off 0.7 | Â | Â | Â | Â |
Accuracy | 89.88% | 89.35% | 76.83% | 70.94% |
Sensitivity | 94.28% | 93.87% | 75.18% | 68.09% |
Specificity | 87.98% | 87.39% | 77.38% | 71.90% |
Pearson | 0.658 | 0.6594 | 0.4816 | 0.4021 |
ROC | 0.975 | 0.9698 | 0.8333 | 0.7557 |
Cut-Off 0.6 | Â | Â | Â | Â |
Accuracy | 90.79% | 89.47% | 75.04% | 70.59% |
Sensitivity | 91.27% | 89.49% | 74.72% | 67.42% |
Specificity | 90.33% | 89.45% | 75.20% | 72.06% |
Pearson | 0.8298 | 0.8264 | 0.4699 | 0.3944 |
ROC | 0.9735 | 0.9686 | 0.8169 | 0.7381 |
Cut-Off 0.5 | Â | Â | Â | Â |
Accuracy | 90.25% | 89.43% | 77.18% | 70.23% |
Sensitivity | 88.33% | 87.70% | 75.57% | 68.78% |
Specificity | 93.85% | 92.67% | 78.24% | 71.18% |
Pearson | 0.8288 | 0.8278 | 0.4446 | 0.3851 |
ROC | 0.9751 | 0.97 | 0.8353 | 0.7675 |