From: miTAR: a hybrid deep learning-based approach for predicting miRNA targets
Model: dataset | Accuracy | Sensitivity | Specificity | F-score | PPVc | NPVc | Brier score |
---|---|---|---|---|---|---|---|
DeepMirTar: DeepMirTarRawa | 0.9348 | NA | NA | 0.9348 | NA | NA | NA |
miTAR1: DeepMirTar Test set | 0.9781 | 0.9648 | 0.9921 | 0.9783 | 0.9922 | 0.9641 | 0.0214 |
miTAR1: DeepMirTar (30 times)b [95% CI] | 0.9787 [0.9714–0.9836] | 0.9717 [0.9615–0.9801] | 0.9857 [0.9759–0.9921] | 0.9786 [0.9717–0.9837] | 0.9858 [0.9756–0.9922] | 0.9719 [0.9610–0.9807] | 0.0193 [0.0144–0.0265] |
miRAW: miRAWRawa | 0.935 | 0.935 | 0.938 | 0.935 | NA | NA | NA |
miTAR2: miRAW Test set | 0.9654 | 0.9609 | 0.9697 | 0.9652 | 0.9695 | 0.9613 | 0.0283 |
miTAR2: miRAW (30 times)b [95% CI] | 0.9649 [0.9601–0.9686] | 0.9616 [0.9562–0.9678] | 0.9683 [0.9618–0.9740] | 0.9651 [0.9604–0.9693] | 0.9687 [0.9623–0.9742] | 0.9610 [0.9558–0.9676] | 0.0271 [0.0246–0.0296] |