Machine Learning Approaches for Epitope Prediction

The development of reliable epitope prediction tools is not feasible in the absence of high quality data sets. Unfortunately, most of the existing epitope benchmark data sets are comprised of epitope sequences that share high degree of similarity with other peptide sequences in the same data set. We demonstrate the pitfalls of these commonly used data sets for evaluating the performance of machine learning approaches to epitope prediction. Finally, we propose a similarity reduction procedure that is more stringent than currently used similarity reduction methods.

Based on our results, we propose FBCPred, a method for predicting flexible
length linear B-cell epitopes using string kernels. The results have been
published in the 7th International Conference on Computational Systems Bioinfor
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