Interpretable machine learning framework reveals microbiome features of oral disease

Abstract

BackgroundAlthough the oral microbiome plays an important role in the progression of oral diseases, the microbes closely related to these diseases remain largely uncharacterized. ResultsWe collected saliva samples from 140 individuals and performed 16 S amplicon sequencing. An interpretable machine learning framework for imbalanced high-dimensional big data of clinical microbial samples was developed to identify 14 oral microbiome features associated with oral diseases. Microbiome risk scores (MRSs) with the identified features were constructed with SHapley Additive exPlanations (SHAP). Correlations of the MRSs with individual physiological indicators and lifestyle habits were calculated. ConclusionOur results reveal a set of oral microbiome features associated with oral diseases. Our study demonstrates the feasibility of preventing oral disease through lifestyle interventions and provides a reference method for the era of precision medicine aimed at individualized medicine.