We then analyzed the estimated coefficients of the logistic regressions in order to further investigate which was the most reliable between the two models. In particular, the estimated coefficients of both models are reported in Table 2 , columns 4 and 5. These coefficients have been evaluated on the basis of the available knowledge but also on the basis of the correlation of each feature with the class, as computed in the training set (Single Regression Coefficients -SRCs). The second column of Table 2 reports the regression coefficient computed for each attribute, while column 3 reports its F-measure. N Every SRC corresponding to an alignment-based feature has the expected sign, given its definition: SRCs are negative for MaxScore_0 and DiffMaxScore features, while they are positive for MaxScore_1. This is confirmed by the corresponding values of the regression coefficients for model B (see Table 2 at column 4). However, model C has an unexpected negative value related to MaxScore_1_nw (see Table 2 at column 5)., Both models confirm that local alignment is more useful for classification than global alignment. Based on all these considerations, we selected model B as the best final model, even if model C had a higher F-measure.