Fig. 4

Classifying paralogs and isoforms using machine learning methods. a AUC comparison between the SVM and RF models using nine RNA-seq datasets from human, zebrafish, and wheat tissues. AL is the aleurone layer, TC is transfer cells, and WE is whole endosperm. b Performance assessment of our method, PIC-me, and a pre-existing method, IsoSVM. Accuracy, positive predicted value (PPV), negative predicted value (NPV), and MCC were calculated as follows: Accuracy = (TP + TN)/(TP + FP + TN + FN), PPV = TP/(TP + FP), NPV = TN/(TN + FN), and MCC = \(\frac{TP \times TN-FP \times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\), where TP and TN are true positive and true negative, respectively, and FP and FN are false positive and false negative, respectively. P-values were calculated using the Mann–Whitney U test. Error bars indicate the standard error of the mean