Subsequently, each set of important features are combined with the machine-learning algorithms (KNN, SVM, GBM, RF, DNN2, and DNN3) to train the models respectively. The training process is nearly identical to the aforementioned model-training without the feature selection. The only difference is existed in the configuration of DNN: DNN2 with two hidden layers (input layer: X (512, 256, or 128) neurons; hidden layer1: X neurons; hidden layer2: X neurons; output layer: 2 neurons) and DNN3 with three hidden layers (input layer: X (512, 256, or 128) neurons; hidden layer1: X neurons; hidden layer2: X neurons; hidden layer3: X neurons; output layer: 2 neurons). Thus, the combination of three sets of important features and six machine-learning methods (KNN, SVM, RF, GBM, DNN2, and DNN3), different random data-splitting schemes (three splits for DNN2/DNN3 and nineteen splits for the others) and four ECFPs will lead to 984 models.