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Table 5 Performance evaluation metrics for the unified model, miTAR, using MirTarRAW test datasets and two independent test datasets

From: miTAR: a hybrid deep learning-based approach for predicting miRNA targets

Model: dataset Accuracy Sensitivity Specificity F-score PPV NPV Brier score
miTAR: MirTarRAW Test set 0.9627 0.9591 0.9663 0.9627 0.9664 0.9589 0.0321
miTAR: MirTarRAW (30 times)a [95% CI] 0.9549 [0.9496–0.9610] 0.9538 [0.9418–0.9629] 0.9560 [0.9428–0.9657] 0.9548 [0.9489–0.9610] 0.9559 [0.9443–0.9662] 0.9540 [0.9424–0.9623] 0.0393 [0.0340–0.0438]
miTAR: DeepMirTarLeft 0.9770 0.9706 0.9844 0.9783 0.9862 0.9668 0.0200
miTAR: miRAWLeft 0.9476 0.9500 0.9452 0.9485 0.9471 0.9482 0.0440
miTAR: DeepMirTarIn 0.9375 0.9375 NAb NAb NAb NAb 0.9254
miTAR: miRAWIn 0.9505 0.9494 0.9517 0.9514 0.9535 0.9474 0.0416
  1. aEvaluation was done by randomly running on the MirTarRAW datasets 30 times. The average value and the 95% confidence interval (given in []) are reported here. Details are in “Results” section
  2. bBecause DeepMirTarIn only contains positive miRNA:target pairs, the specificity, F-Score, PPV and NPV cannot be calculated. NA represents the value is not available