用广义选择性神经网络集成预测MHC-II分子结合肽

MHC II类分子结合肽的预测对于免疫研究和疫苗设计非常重要，然而其结合肽长度的可变性等原因使其预测变得极为困难，提出了一种基于广义选择性神经网络集成的MHC II分子结合肽预测算法，该算法是一种双层集成模型。第一层是用微分进化算法去生成初始神经网络集成池，第二层是从初始神经网络集成池中选择部分组成最终的神经网络集成。实验结果表明广义选择性神经网络集成比传统的选择性神经网络有更好的泛化性能。

Using generalized selective neural network ensemble to predict 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 and other aspects of each binding peptide complicate this prediction.In this paper,generalized 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 differential evolution is used to build some NNEs.The second-level ensemble is that a subset of primary NNEs is selected to make up the final ensemble.Experiment results indicate that the generalized ensemble model has better generalization and performance compared to traditional selective neural network ensemble.