Line Plot from Scientific Research

ROC curves for random forest classifiers constructed from differentially abundant features in each of the five datasets (colored in red) are compared to the ROC curve for a multi-omics classifier (colored in blue). The multi-omics classifier was constructed from transcripts that were differentially abundant in both the metatranscriptome and predicted metagenome, significantly upregulated transcripts in enriched pathways, and differentially abundant metabolites associated with microbial community metabolic potential. All classifiers were trained on 60% of the dataset and tested on the remaining 40% of samples (n = 230 with all three data types). Colored areas indicate the 95% confidence intervals of the ROC curves. P values for the AUC of single dataset classifiers compared to the multi-omics classifier were calculated by bootstrapping.

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