We implemented and trained DL and LR binding classification models. We then selected the best training epoch in terms of F1 Score, the harmonic mean of precision and recall, on the validation set, since it can handle class imbalance. Finally, we tested the selected models on the corresponding test set of each cross-validation strategy. Stratified subsampling of the 80% of the test data was used to sample 100 values from the performance estimates distributions. The nonparametric Wilcoxon rank-sum test was used to compare AUC and BEDROC (20) metrics between all pairs of models. P-values were adjusted for multiple testing by computing the False Discovery Rate (FDR) by Benjamini-Hochberg 60 for each metric.