Figure 4.

NOTCH-MYC-based models outperform their agnostic counterparts in predicting response to neoadjuvant chemotherapy in patients with triple-negative breast cancer

Models were trained on 1000 bootstraps of the training data (transparent colors) and evaluated on the untouched testing data (solid colors) using the Area Under the ROC curve (AUC) as metric. Mechanistic models were based on the NOTCH-MYC mechanism (241 pairs) (purple) while agnostic models were trained either using the top differentially expressed genes (500 genes) (green) or the corresponding pairwise comparisons (250 pairs) (yellow). Shown are the smoothed density distributions of the AUC values with each panel corresponding to one of the four algorithms used. KTSP: K-top scoring pairs; RF: random forest; SVM: support vector machine; XGB: extreme gradient boosting; DEGs: differentially expressed genes; TNBC: triple-negative breast cancer.