Hybrid Selective Neural Network Ensembles for Prediction of MHC Class II-Binding Peptides

Predictions of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules are important for immunology research and vaccine design. The variable length of each binding peptide complicates this prediction. In this paper, hybrid selective neural network ensemble is proposed for prediction of MHC class II-binding peptides, the ensemble is built on two-level ensemble architecture .The first-level ensemble is used to create primary neural network ensemble(NNE),where migration differential evolution-based selective neural network and GASEN are used to build some NNEs.The second-level ensemble is that primary NNEs are selected to make up the final ensemble. Experiment results indicate that the hybrid ensemble model has better generalization and performance compared to any of individual neural networks and traditional selective neural network ensemble.