Fig. 3

The proposed CNN and RNN architectures for pre-miRNAs prediction. a. CNN model. The pre-miRNA sequence is treated as a 180 × 12 × 1 vector. There are three cascades of convolution and max-pooling layers followed by two fully connected layers. The shapes of the tensors in the model are indicated by height × width × channels. FC: fully connected layer with 32 units. b. RNN model. Three LSTM layers with 128, 64 and 2 units respectively are shown in the RNN. The final output is passed through a softmax function with the output of probability distribution over labels. In each time step along the pre-miRNA sequence, the LSTM cells remembered or ignored old information passed along the arrows. The output was the probability distribution over the true or false labels.