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

Clicks: 306
ID: 118102
2020
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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.
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fernández-edreira2020proceedingsidentification Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Diego Fernández-Edreira;Jose Liñares-Blanco;Carlos Fernandez-Lozano;Fernández-Edreira, Diego;Liñares-Blanco, Jose;Fernandez-Lozano, Carlos;
Journal proceedings
Year 2020
DOI
10.3390/proceedings2020054050
URL
https://www.mdpi.com/2504-3900/54/1/50
Keywords
Machine learning Diabetes metagenomics data science microbiome

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