Multi classifier integration approach for T cell epitope prediction
用于T细胞表位预测的分类器集成方法*

Predicting which peptides can bind to a specific MHC molecule is indispensable to minimizing the number of peptides required to synthesize, to the development of vaccines, and especially to aiding to understand the specificity of T cell mediated immunity. In order to make up for the disadvantage of the existing T cell epitope prediction methods based on machine learning in understandability, a decision table comprising the nonamers was constructed by peptide preprocessing, then the multi classifier integration algorithm based on rough sets was proposed, which took advantage of expert knowledge of binding motifs and diverse attribute reduction algorithms. Finally, with the help of the RSEN, the comprehensible rule set ensemble with strong generalization ability to predict the peptides that bind to HLA DR4(B1*0401) was acquired.