Identification of Prevotella, Anaerotruncus and Eubacterium Genera by Machine Learning Analysis of Metagenomic Profiles for Stratification of Patients Affected by Type I Diabetes

Abstract

[Abstract] Previous works have reported different bacterial strains and genera as the cause of different clinical pathological conditions. In our approach, using the fecal metagenomic profiles of newborns, a machine learning-based model was generated capable of discerning between patients affected by type I diabetes and controls. Furthermore, a random forest algorithm achieved a 0.915 in AUROC. The automation of processes and support to clinical decision making under metagenomic variables of interest may result in lower experimental costs in the diagnosis of complex diseases of high prevalence worldwide.