We considered three different subsets of such attributes, based on different feature selection strategies. As a last step, we learned some simple classifiers, which have been evaluated with a leaveone-out cross-validation approach. Since the final aim of the EAR challenge is to extract rules able to explain the propensity of peptide to react, we selected classifiers able to provide a model easy to be interpreted (e.g. logistic regression, rule learners, decision trees, etc.).