As mentioned in the previous section, we have exploited a machine learning approach to analyze the data, after a knowledgebased feature generation phase. In particular we extracted two types of features for every peptide: (i) sequence-dependent features, which are based on both general information about peptides and knowledge about the propensity of a peptide to interact (amino acid frequencies, antigenicity, accessibility, etc.); (ii) datasetdependent features, which are generated by exploiting the scores obtained by aligning every peptide of the training set with all the others with both global and local alignment. A total of 37 features have been finally generated.