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Table 9 Comparative Results using Benchmark Dataset 1 for RF, RNN, CNN-ext. and CNN. RF refers to Random Forest. RNN refers to Recurrent Neural Network. CNN-ext. refers to extended CNN where we use our original architecture with another convolutional layer and max pooling layer adding after the original max pooling layer. CNN refers to the Convolutional Neural Network described in the paper

From: CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes

# Class/Group Training Independent Test
RF RNN CNN-ext CNN RF RNN CNN-ext CNN
1 Level 1 0.97 0.43 0.95 0.96 0.95 0.70 0.69 0.70
2 Class A 0.97 0.16 0.97 0.98 0.75 0.70 0.78 0.78
3 Class B/Group 3 0.94 −0.04 0.96 0.96 0.94 0.34 1.00 0.99
4 Class C/Group 1 0.92 0.20 0.96 0.97 0.90 0.54 0.89 0.89
5 Class D 1.00 0.66 0.99 1.00 0.44 0.06 1.00 0.91
6 Group 2 0.96 0.42 0.97 0.97 0.75 0.34 0.81 0.81