Fig. 1

Overview of elastic net model development process including feature reduction and model selection stages. The graphic includes the nested cross-validation process which is used for both internal validation (comparison of models within the Dunedin data set) and to find an optimal number of features from the ranking-based filter methods. For independent test set evaluation, training is conducted on the full Dunedin data set which is then used for TL estimation in test sets. See “Modelling overview” and “Feature-selection methods” Sections for further details. MAE Mean absolute error, CV Cross-validation, FDR False discovery rate, TL Telomere length