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

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UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitle3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020es_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.grupoInvRedes de Neuronas Artificiais e Sistemas Adaptativos -Informática Médica e Diagnóstico Radiolóxico (RNASA - IMEDIR)es_ES
UDC.issue1es_ES
UDC.journalTitleProceedingses_ES
UDC.startPage50es_ES
UDC.volume54es_ES
dc.contributor.authorFernández-Edreira, Diego
dc.contributor.authorLiñares Blanco, José
dc.contributor.authorFernández-Lozano, Carlos
dc.date.accessioned2020-10-27T18:11:45Z
dc.date.available2020-10-27T18:11:45Z
dc.date.issued2020-08-27
dc.description.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.es_ES
dc.description.sponsorshipThis work was supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.” and the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23) and Competitive Reference Groups (Ref. ED431C 2018/49). The funding body did not have a role in the experimental design; data collection, analysis and interpretation; and writing of this manuscriptes_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; ED431D 2017/16es_ES
dc.description.sponsorshipXunta de Galicia; ED431D 2017/23es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2018/49es_ES
dc.identifier.citationFernández-Edreira, D.; Liñares-Blanco, J.; Fernandez-Lozano, C. Identification of Prevotella, Anaerotruncus and Eubacterium Genera by Machine Learning Analysis of Metagenomic Profiles for Stratification of Patients Affected by Type I Diabetes. Proceedings 2020, 54, 50. https://doi.org/10.3390/proceedings2020054050es_ES
dc.identifier.doi10.3390/proceedings2020054050
dc.identifier.issn2504-3900
dc.identifier.urihttp://hdl.handle.net/2183/26559
dc.language.isoenges_ES
dc.publisherMDPI AGes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016/PI17%2F01826/ES/PROYECTO COLABORATIVO DE INTEGRACION DE DATOS GENOMICOS (CICLOGEN). TECNICAS DE DATA MINING Y DOCKING MOLECULAR PARA ANALISIS DE DATOS INTEGRATIVOS EN CANCER DE COLON/
dc.relation.urihttps://doi.org/10.3390/proceedings2020054050es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectDiabeteses_ES
dc.subjectMachine learninges_ES
dc.subjectMicrobiomees_ES
dc.subjectMetagenomicses_ES
dc.subjectData sciencees_ES
dc.titleIdentification of Prevotella, Anaerotruncus and Eubacterium Genera by Machine Learning Analysis of Metagenomic Profiles for Stratification of Patients Affected by Type I Diabeteses_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
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