Extended Data Fig. 1: Performance of Gradient Boosting Regression (GBR) models, related to Fig. 1.

(a) We defined prediction accuracy (ρ_GBR) as the Spearman correlation between observed and predicted relative fold-changes. (b) Significance of the prediction accuracy using t-distribution for two-sided significance test for Spearman correlation⁶⁶. (c) Density map of scatter plot of observed and predicted relative fold-changes obtained from the cerebella of 9-months old mice. There is clear correlation between the predicted and observed relative fold-changes, which we quantify through the GBR prediction accuracy (ρ_GBR). (d) Comparison of prediction accuracy following Monte Carlo cross-validation, the default cross-validation scheme of this manuscript, against predication accuracy following four-fold cross-validation for individual combinations of tissues and ages (black dots). In Monte Carlo cross-validation a given gene will on average be used nine times for developing the gradient boosting regression, and on average once for quantifying its performance. In four-fold cross-validation each gene will be considered exactly three times for developing the gradient bossing regression, and exactly once for quantifying its performance.