Skip to main content
Fig. 2 | BMC Bioinformatics

Fig. 2

From: Integration of metabolomics, lipidomics and clinical data using a machine learning method

Fig. 2

Methodological work flow for data integration. Random forest (RF) classification was used to select subsets of metabolites from the combination of all metabolite data sets. Data sets which are the best in predicting the dose of PPAR-pan administered were assessed by calculating classification error values. The variables from the individual datasets were selected by a backward elimination approach, and the final set of metabolites were used for network analysis. As a separate strategic workflow, an RF regression approach was used to link liver metabolites with classical clinical chemistry parameters. Datasets which explain the variation of the classical clinical chemistry parameters were calculated, and individual variables were selected using permutation tests. Again, the final set of metabolites and the explained clinical chemistry parameters were selected for network analysis

Back to article page
\