PMC

Random forest (RF) supervised class prediction using patients’ global metabolomic expression profiles. Mean decrease accuracy (MDA) denotes the per cent decrease in accuracy of the random forest analysis when the trial is performed in the absence of the indicated biomarker. (A) Utility of RF to accurately classify normal healthy controls (NHC), DC and HCC patients into their appropriate groups on the basis of their global metabolomic profiles. With all three groups included, 2-pyrrolidinone, a GABA metabolite, possessed a MDA of 8%, making it the most important metabolite for RF accuracy. (B) RF analysis of HCC patients (n = 30) vs. disease control patients with HCV-associated cirrhosis (DC, n = 27). Several amino acids ranked among the most important metabolites for the RF class prediction accuracy of HCC vs. DC, with serine being the most important metabolite for this predictive accuracy.