Bidirectional LSTM and CNN model for predicting DNA-protein binding

The invention provides a bidirectional LSTM and CNN model for predicting DNA-protein binding. The model includes an input layer, a BLSTM layer, a convolutional layer, a maximum pooling layer, a full connection layer, and an output layer. Each input sequence is expressed as a four-line binary matrix by the input layer through single thermal coding. In the BLSTM layer, each LSTM model in a previouslayer will receive information of interest on DNA from an input sequence, and encode and interpret contributions from past historical information to a hidden state; then, the BLSTM module is propagated to the next BLSTM module, wherein a matrix scanned and input by each convolution kernel in the convolution layer is used for motif discovery, and information with different intensities is associatedwith potential sequence patterns; the maximum pooling layer is used for maximizing an output signal of each convolution kernel to form a complete sequence; the output layer performs non-linear conversion to determine DNA-prot