The performance of the AMP classifiers on the considered datasets is reported in table . The results are averaged over the multiple test sets generated by nested-cross validation. On both datasets, the proposed models achieve a greater average accuracy and AUC than the baseline, with the local alignment score achieving the best values in all cases. The Welch t-test , with p = 0.05, is used to compare the test set AUC of our proposed models against the baseline across the outer folds of nested cross-validation. Adjusting for the testing of multiple hypotheses with the Bonferroni correction, we observe a significant difference between the mean AUC of the local alignment score and that of the baseline on the AMPScan dataset. All other comparisons, including those on the DeepAMP dataset, are not significant. The performance of all models is greater on the AMPScan dataset than on the DeepAMP dataset.