Fig. 5: Including the covariates in metadata (age, BMI, and gender) in the input of McMLP improves it in terms of predicting endpoint metabolomic profiles on real data from the avocado intervention study.

All results are derived from McMLP. We either included (“w/ b” label) or did not include (“w/o b” label) baseline metabolomic profiles as input variables. Each method with a particular combination of input data is colored the same in all panels. Standard errors are computed based on fifty random train-test splits and shown in all panels (solid black vertical lines). To compare different methods, we adopted three metrics: the mean Spearman Correlation Coefficient (SCC) \(\bar{\rho }\), the fraction of metabolites with SCCs greater than 0.5 (denoted as \({f}_{\rho > 0.5}\)), and the mean SCC of the top-5 predicted metabolites \({\bar{\rho }}_{5}\). Error bars denote the standard error (n = 50). a1-a3, Comparison of the performance in predicting SCFAs on the data from the avocado intervention study²⁸. b1-b3, Comparison of performance in predicting bile acids on the data from the avocado intervention study²⁸. All statistical analyses were performed using the two-sided Wilcoxon signed-rank test. P values obtained from the test are divided into four groups: (1) \(p > 0.05\) (n.s.), (2) \(0.01 < p\le 0.05\) (*), (3) \({10}^{-3} < p\le 0.01\) (**), and (4) \({10}^{-4} < p\le {10}^{-3}\) (***). Source data of raw data points and p values are provided as a Source Data file.