Based on the selected genes, 101 model combinations derived from the following 10 machine-learning algorithms were constructed to develop BCR prognostic models for prostate cancer: Lasso, Ridge, Enet (Elastic Net), StepCox (stepwise Cox), survivalSVM (survival support vector machine), CoxBoost, SuperPC (supervised principal component), plsRcox (partial least squares regression for Cox), RSF (random survival forest), and GBM (generalized boosted regression modeling) (Figure 4A). The gene discussed is BCR; the disease is Familial prostate cancer.