Fig. 3

Model construction and performance. A Flow chart for microbial model construction. B Venn diagram shows the overlap of important microbiota assigned at species level between irAEs and response. C The AUC of the optimized models constructed with the most important microbiota for distinguishing non-irAEs from irAEs. Mean AUC and standard deviation of stratified tenfold cross-validation were shown. For each AUC detailed: “ROC Fold 1 (AUC = 0.89),” “ROC Fold 2 (AUC = 0.93),” “ROC Fold 3 (AUC = 0.97),” “ROC Fold 4 (AUC = 1.00),” “ROC Fold 5 (AUC = 0.94),” “ROC Fold 6 (AUC = 0.97),” “ROC Fold 7 (AUC = 0.91),” “ROC Fold 8 (AUC = 0.57),” “ROC Fold 9 (AUC = 0.71),” “ROC Fold 10 (AUC = 0.95).” D Prediction performance of important features using study-by-study and leave-one-study-out (LOSO) validation. The heatmap shows the area under the receiver operating characteristic curve (AUROC) from cross-validations within each study (blue boxes along the diagonal) and study-to-study model transfer (external-validations off-diagonal). The last column shows the average AUROC for study-to-study predictions. The bottom line shows the AUROC for a model trained on all studies but one (LOSO validation)