Figure 3From: Large-scale prediction of long disordered regions in proteins using random forestsROC curves of 10-fold cross validation tests. The ROC curves of IUPforest-L in 10-fold cross validation tests are shown. The IUPforest-L could reach a 76% true positive rate at a 10% false positive rate with MCC = 0.67, Sproduct = 0.64 and an area of 89.5% under the ROC curve on the training data set with a window of 31 aa.Back to article page