排序学习前向掩蔽模型在T细胞表位预测中的应用

在综述了T细胞表位预测的定义，意义和研究现状的基础上，分析了当前流行的基于误差反向传播前馈神经网络(BPNN)的T细胞表位预测模型的不足，即网络结构较难确定、训练速度慢和难以增量学习等，提出了利用排序学习前向掩蔽（SLAM）模型及其增量学习算法作为T细胞表位预测方法，并给出了构建T细胞表位预测模型的基本步骤。基因HLA-DR4 (B1*0401)编码的MHC II类分子结合肽的应用实例表明，与基于BPNN的T细胞表位预测模型相比，基于SLAM的T细胞表位预测模型不但能在极短时间内完成样本的学习,而且能有效地实现增量学习。

Applications of sequential learning ahead masking model to T cell epitope prediction

The definition,the meaning and the state-of-art of T cell epitope prediction were firstly summarized.And then,the disadvantages of the prevailing T cell epitope prediction model based on the Back-Propagation Neural Networks(BPNN),including difficulties in presetting networks structure,converging and incremental learning,were investigated.In terms of the above-mentioned drawbacks,Sequential Learning Ahead Masking model(SLAM) and its fast incremental learning algorithm were deliberately chosen to predict T cell epitope.Meanwhile,the basic steps of constructing T cell epitope prediction model based on SLAM were advocated.Finally,a case study of predicting the binding capacities to MHC class II molecule encoded by gene HLA-DR4(B1*0401) was given in detail.The application results show that T cell epitope prediction model based on SLAM has better learning performance and stronger incremental learning capabilities than that based on conventional BPNN.