Fig. 1

The framework diagram of EL_PSSM-RT. EL_PSSM-RT contains 4 steps. The first step is to divide the non-binding residues in the training dataset into n subsets and to construct n new training datasets by combining the n subsets of non-binding residues and binding residues individually. The secondary step is to extract the three categories of features for all the residues. The third step is to train both SVM classifier and Random Forest classifier by each category of features on every training subset. The fourth step is to use a dynamic ranking and selecting method to select the based predictors with the largest diversity between each other to build the ensemble predictor