Figure 2 .

The parallel training process of Deepbind [43]. (A) The DeepBind model processes five independent sequences in parallel. The data first passes through the convolutional layer to extract features, then passes through the pooling layer to optimize the features. Finally, features go through the activation function to output the prediction result and compare with the target to calculate the loss and update weight to improve the prediction accuracy. (B) It is shown in detail that the dataset is divided into validate set, train set and test set, which are used to calculate validate AUC (area under the curve), training AUC and test AUC, respectively, to select the optimal parameters.