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dc.contributor.authorLiñares Blanco, José
dc.date.accessioned2022-06-29T11:28:32Z
dc.date.available2022-06-29T11:28:32Z
dc.date.issued2022-05-17
dc.identifier.citationLiñares-Blanco J... [et al.] (2022) Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes. Front. Microbiol. 13:872671. doi: [10.3389/fmicb.2022.872671]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/75722
dc.description.abstractInflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role ofmicroorganims in this disease development. Thanks to the open access to multiple omics data, it is possible to develop predictive models that are able to prognosticate the course and development of the disease. The interpretability of these models, and the study of the variables used, allows the identification of biological aspects of great importance in the development of the disease. In this work we generated a metagenomic signature with predictive capacity to identify IBD from fecal samples. Different Machine Learning models were trained, obtaining high performance measures. The predictive capacity of the identified signature was validated in two external cohorts. More precisely a cohort containing samples from patients suffering Ulcerative Colitis and another from patients suffering Crohn’s Disease, the two major subtypes of IBD. The results obtained in this validation (AUC 0.74 and AUC = 0.76, respectively) show that our signature presents a generalization capacity in both subtypes. The study of the variables within the model, and a correlation study based on text mining, identified different genera that play an important and common role in the development of these two subtypes.es_ES
dc.description.sponsorshipCollaborative Project in Genomic Data Integration (CICLOGEN) - Carlos III Health Institute from the Spanishes_ES
dc.description.sponsorshipNational plan for Scientific and Technical Research and Innovation 2013-2016 PI17/01826es_ES
dc.description.sponsorshipEuropean Regional Development Funds (FEDER)-A way to build Europees_ES
dc.description.sponsorshipSpanish Government RYC2019-026576-Ies_ES
dc.description.sponsorshipUK Research & Innovation (UKRI)es_ES
dc.description.sponsorshipBiotechnology and Biological Sciences Research Council (BBSRC) BB/S006281/1es_ES
dc.description.sponsorshipQueen's University of Belfast UKRI block grantes_ES
dc.language.isoenges_ES
dc.publisherFrontierses_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectMachine learninges_ES
dc.subjectFeature selectiones_ES
dc.subjectInflammatory bowel diseasees_ES
dc.subjectMicrobiomees_ES
dc.subjectChron's diseasees_ES
dc.subjectUlcerative colitis es_ES
dc.titleMachine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypeses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3389/fmicb.2022.872671
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España