Fig. 2

A weighted approach improves 2D kernel age predictions. A 2D kernel age prediction model was generated for 27 CpGs (R² > 0.7 in the training set). The model was trained on a subset of samples from the training set with uniform age distribution. When we applied the model to the validation dataset, the predictions were not reliable. Pearson squared correlation R² and median absolute error (MAE) are indicated. B The same CpGs were used to generate multivariate models based on the entire training set. C Alternatively, for each of the age-associated CpGs, a linear regression model was established to facilitate single CpG predictions. When we averaged these predictions, the performance was much lower than for the multivariable model. D The 2D kernel age-prediction model was further optimized by optimized weights for individual CpGs, which were determined by genetic algorithm optimization. E Heatmap of Pearson squared correlation (R²) between chronological and predicted age for WKDE and a set of widely used clocks, split in all datasets used for training and validation. F Heatmap of MAE for WKDE and the same set of clocks as in E, split in all datasets used for training and validation