Fig. 2

a Precision-recall curve—the precision-recall metric indicates the relationship between the model’s positive predictive value (precision) and sensitivity (recall) at various thresholds. b Receiver Operating Characteristic (ROC) curve—the ROC metric is calculated as a ratio between the true positive rate and the false positive rate at various thresholds. Both the metrics, precision-recall and ROC calculated by ENNGene, are adjusted for multi-class classification problems and thus can be applied to models with any number of classes. Both curves and other metrics (accuracy, loss, AUROC) are a standard part of exported results after a model evaluation, optionally with Integrated Gradients’ scores. c Integrated Gradients visualization—IG scores of ten sequences with the highest predicted score per class are directly visualized in the browser. Scores are displayed in separate rows for each input type used—sequence, secondary structure, and conservation score. The higher the nucleotide’s attribution to the prediction of a given class, the more pronounced is its red color. On the other hand, the blue color means a low level of attribution