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Table 3 Neural network performance to discriminate between proteins binding to different types of RNA based on charge, dipole and quadrupole moments*.

From: Analysis of electric moments of RNA-binding proteins: implications for mechanism and prediction

Positive class binding to Negative class binding to Number of proteins in + ve class Number of proteins in -ve class AUC F1 Precision Recall Accuracy
RNA NB 160 2441 0.78 0.37 0.31 0.45 0.91
rRNA NB 84 2441 0.79 0.26 0.23 0.30 0.94
tRNA NB 20 2441 0.42 0.02 0.01 1.00 0.03
vRNA NB 17 2441 0.75 0.24 0.24 0.24 0.99
mRNA NB 13 2441 0.10 0.01 0.01 1.00 0.02
tRNA rRNA 20 84 0.70 0.45 0.32 0.75 0.64
mRNA rRNA 13 84 0.56 0.30 0.18 1.00 0.37
vRNA rRNA 17 84 0.44 0.32 0.19 1.00 0.28
mRNA tRNA 13 2441 0.07 0.57 0.39 1.00 0.39
mRNA vRNA 13 2441 0.02 0.60 0.43 1.00 0.43
tRNA vRNA 20 17 0.19 0.63 0.46 1.00 0.46
DNA NB 143 2441 0.72 0.22 0.20 0.26 0.90
RNA DNA 160 143 0.58 0.69 0.53 1.00 0.53
rRNA DNA 84 143 0.74 0.64 0.52 0.83 0.65
tRNA DNA 20 143 0.33 0.24 0.13 1.00 0.20
mRNA DNA 13 143 0.07 0.16 0.09 1.00 0.14
  1. * AUC is area under the ROC curve, F-measure (F1) is the highest geometric mean of precision and recall and accuracy is number of correct predictions relative to all predictions at peak F-measure. In all cases, neural network with three units in the hidden layer was used for training in a leave-one-out procedure and the training was performed for a fixed number of epochs without using information from left-out protein.