Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
Fig 6
AUC by cross-stage and cross-study analysis for T2D discrimination in the T2D and WT2D datasets.
When the training (TR) and test (TS) sets coincide, the analysis was done in cross-validation (with the margin of error reported in parenthesis). In the other cases, the model was generated on TR and then applied to TS. In bold we report the best value for each setting and feature type (i.e., species abundance or marker presence), and circled are the overall best value for each scenario.