The classification performance was quantified using precision, recall, specificity, Matthews Correlation Coefficient (MCC), ROC-AUC and PR-AUC. Our selection ensures that our evaluation encompasses all aspects of a given classifier's performance and is robust to class imbalance. To assess whether the any fingerprint was ranked differently than the others across all datasets, we first performed a Friedman test for each classification metric and classification model. If the outcome of the Friedman test was statistically significant (α<0.05), we then performed post-hoc tests (2-tailed Wilcoxon signed rank test with Benjamini-Hochberg correction, α<0.05) to identify which fingerprint pair was significantly different. Hardware and software