High-performing cross-dataset machine learning reveals robust microbiota alteration in secondary apical periodontitis.

ABSTRACT: Multiple research groups have consistently underscored the intricate interplay between the microbiome and apical periodontitis. However, the presence of variability in experimental design and quantitative assessment have added a layer of complexity, making it challenging to comprehensively assess the relationship. Through an unbiased methodological refinement analysis, we re-analyzed 4 microbiota studies including 120 apical samples from infected teeth (with/without root canal treatment), healthy teeth, using meta-analysis and machine learning. With high-performing machine-learning models, we discover disease signatures of related species and enriched metabolic pathways, expanded understanding of apical periodontitis with potential therapeutic implications. Our approach employs uniform com

SUBMITTER: Li H 

PROVIDER: S-EPMC11224960 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature