Fig. 4

Prediction of ICI outcomes using Stem.Sig. A Flow chart of training, validating, and testing the Stem.Sig model constructed using machine learning process. In the training set, we applied 10-time repeated 5-fold cross-validation for parameters tuning of different machine learning algorithms. In the validation set, Naïve Bayes algorithm with best AUC was kept as the final Stem.Sig model. (parameter: fL=0; adjust = 0.75; useKernel = TRUE). B Comparison of multiple ROC plot depicting the performance of different machine learning algorithms in the validation set. C ROC plot depicting the performance of the final Stem.Sig model in validation and testing cohort. D Kaplan-Meier curves comparing OS between High-risk and Low-risk patients in validation and testing set. “NR” and “R” predicted by the final Stem.Sig Model was defined as “High-risk” and “Low-risk” patients respectively. HR were calculated by Cox proportional hazards regression analysis. Abbreviation: TPR, true positive rate; FPR, false positive rate; AUC, area under the curve; HR, hazard ratio; CI, confidence intervals