Figure 1.

Building mechanistic classifiers by embedding prior knowledge in the predictive decision rules

Three different cancer cases were considered: predicting bladder cancer progression, predicting the response to neoadjuvant chemotherapy in patients with triple-negative breast cancer, and predicting prostate cancer metastatic progression. We adopted two different experimental designs: the balanced stratification (training bootstrap) and cross-study validation. In the balanced stratification design, all datasets were pooled together after normalization and preprocessing then split into training and testing sets. The training set was bootstrapped 1000 times and on each resample, we trained agnostic and mechanistic models and then evaluated their performance on the testing set. In the cross-study validation, the analysis included n iterations where n corresponds to the number of studies. In each iteration, we used all, but one study for training agnostic and mechanistic models and then evaluated their performance on the left-out study. k-TSPs: K-top scoring pairs, RF: random forest, SVM: support vector machine, XGB: extreme gradient boosting, DEGs: differentially expressed genes.