**Figure 4.** **Performance of age-predictive models built on various data types.** Age-predictive L1-norm penalized generalized linear models were built using protein and small RNA measurements, either separately or in combinations. Performance was estimated via 10-fold cross-validation with 100 repeats. Prediction errors were determined from predictions based on left-out data (data that was not used to build the model). (**A**–**C**) Performance of the built models: the mean (dot) and standard deviation (circle) of two error metrics are shown: the coefficient of determination (R^{2}) on the x-axis and the Mean Absolute Error (MAE) on the y-axis. The panels compare (**A**) all small RNAs with all proteins, (**B**) the different classes of small RNAs, and (**C**) models combining proteins and small RNAs. (**D**–**F**) Scatter plots of chronological age vs. predicted age are shown for all individuals in the cohort for (**D**) the proteomics-based model, (**E**) the all small RNA-based model, and (**F**) the proteomics and top 20_miRNA-based model. Blue and red lines show, respectively, the identity and linear regression lines. (**G**) Plot of the number of predictive molecules kept in the model (with non-zero coefficients) on the x-axis vs. the mean (line) and standard deviation (shadow) MAE on the y-axis. MAE values were smoothed via a LOESS regression (R loess function with a span argument of 0.6). (**H**) Heatmap showing the correlation of the error in predictions (delta age) for the proteomics-based model and the small RNA-based models with R^{2} > 0.2. (**I**) Absolute standardized coefficients of the proteomics and top 20_miRNA-based models.