Figure 6.

Mechanistic models based on cellular adhesion and oxygen response have similar performance to their agnostic counterparts in predicting prostate cancer metastatic progression

The figure depicts the results from the bootstrap design in which the training set (transparent colors) was resampled 1000 times. On each resample, models were trained to predict metastatic progression in prostate cancer and their performance was evaluated on the untouched testing set (dark colors) using the Area Under the ROC Curve (AUC) as evaluation metric. Mechanistic models were based on the cellular adhesion and O₂ response mechanism (50 pairs) (purple) while agnostic models were trained using either the top differentially expressed genes (100 genes) (green) or the corresponding pairwise comparisons (50 pairs) (yellow). Shown are the smoothed density distributions of the AUC values and each panel corresponds 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.