The framework of PreTP-EL. There are three main steps: (i) feature extraction. Each training or test peptide sequence is represented as nine feature vectors by nine feature extraction methods. (ii) Training phase. The nine feature vectors are fed into SVM and RF basic models for training, and then 18 individual basic predictors are constructed. The training sequences are embedded into the probability matrix based on 18 predictors, where the rows represent the probability scores obtained by the different predictors. Then, the probability matrix is fed into the genetic algorithm for training. The genetic algorithm is applied to learn the weights corresponding to the different predictors. (iii) Test phase. The therapeutic peptide sequence is predicted by Eq. (17).