Skip to main content
[Preprint]. 2023 Jun 29:2023.06.26.546628. [Version 1] doi: 10.1101/2023.06.26.546628

Fig. 5|. Machine learning predicts proteomic changes from metabolomic changes.

Fig. 5|

a, Scheme showing the ML task of predicting proteins from metabolomic data measured in parallel by SMAD. b, Overall performance of all proteins true versus ML predicted values. c,d,e, Examples of three of the most well predicted proteins from different pathways: TPIS from glycolysis, DHB4 from fatty acid oxidation, and RSSA from the ribosome. f, plot of the Spearman rho between true versus predicted protein quantities for all 450 proteins. Red indicates statistically significantly predicted proteins according to a Bonferroni corrected p-value from Spearman correlation analysis. g, WikiPathways term enrichment analysis of the 54 significant proteins from (f).