Fig. 1From: Refining risk prediction in pediatric acute lymphoblastic leukemia through DNA methylation profilingGraphical representation of the study design. The models were trained with data from 763 ALL patients, all of whom had previously been characterized by genome-wide DNA methylation arrays. The dataset was partitioned into a training set (80% of the patients) and a test set (the remaining 20%). The training set was used to identify CpG sites with DNA methylation status associated with two key outcomes: relapse risk and mortality. The selected CpG sites were used to train Random Survival Forests models. Two models were generated: a Relapse Risk Predictor (RRP) and a Mortality Risk Predictor (MRP). The test set was utilized for internal validation. Finally, the models were further validated on two additional datasetsBack to article page