Diversity and random forest models of oral microbiomes in periodontal health using publicly available data

Mapping oral microbes in relation to periodontal health is essential for microbiome-based diagnostics and the development of new preventive/therapeutic strategies. Our two-by-two predictive models demonstrated that a small set of bacterial ASVs can accurately classify periodontally healthy samples according to their oral niche. Notably, models distinguishing saliva from dental plaques achieved superior performance compared to those discriminating between plaques. This likely reflects the greater resemblance in dominant microbial taxa between the two plaque niches. These findings underscore the potential of machine-learning approaches to identify key microbial signatures and highlight the predictive ASVs as promising biomarkers for characterizing oral niches in periodontal health.