An Application of Association Rule Mining to Hla-a*0201 Epitope Prediction

This paper presents a novel approach to epitope prediction based on the clustering of known T-cell epitopes for a given MHC class I allele (HLA-A*0201). A combination of association rules (ARs) and sequence-structure patterns (SSPs) was used to do the clustering of training set epitopes from the Antijen database. A regression model was then built from each cluster and a peptide from the test set was declared to be an epitope only if one or more of the models gave a positive prediction. The sensitivity (TP/TP+FN) of the AR/SSP regression models approach was higher than that of a single regression model built on the entire training set, and was also higher than the sensitivity measures for SYFPEITHI, Rankpep, and ProPred1 on the same test set.