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dc.contributorVall d'Hebron Barcelona Hospital Campus
dc.contributor.authorLiñares Blanco, Jose
dc.contributor.authorFernandez-Lozano, Carlos
dc.contributor.authorSeoane Fernández, Jose Antonio
dc.contributor.authorLopez-Campos, Guillermo
dc.date.accessioned2022-09-09T07:40:40Z
dc.date.available2022-09-09T07:40:40Z
dc.date.issued2022-05-17
dc.identifier.citationLiñares-Blanco J, Fernandez-Lozano C, Seoane JA, López-Campos G. Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes. Front Microbiol. 2022 May 17;13:872671.
dc.identifier.issn1664-302X
dc.identifier.urihttp://hdl.handle.net/11351/8090
dc.descriptionCrohn's disease; Microbiome; Ulcerative colitis
dc.description.abstractInflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims 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.
dc.language.isoeng
dc.publisherFrontiers Media
dc.relation.ispartofseriesFrontiers in Microbiology;13
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScientia
dc.subjectAprenentatge automàtic
dc.subjectIntestins - Inflamació - Diagnòstic
dc.subjectIntestins - Microbiologia
dc.subject.meshMycobiome
dc.subject.meshInflammatory Bowel Diseases
dc.subject.mesh/diagnosis
dc.subject.meshMachine Learning
dc.titleMachine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.3389/fmicb.2022.872671
dc.subject.decsmicobioma
dc.subject.decsenfermedad inflamatoria intestinal
dc.subject.decs/diagnóstico
dc.subject.decsaprendizaje automático
dc.relation.publishversionhttps://doi.org/10.3389/fmicb.2022.872671
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.audienceProfessionals
dc.contributor.organismesInstitut Català de la Salut
dc.contributor.authoraffiliation[Liñares-Blanco J] Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC, University of A Coruña, A Coruña, Spain. GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government PTS Granada, Granada, Spain. Department of Statistics and Operational Research, University of Granada, Granada, Spain. [Fernandez-Lozano C] Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC, University of A Coruña, A Coruña, Spain. [Seoane JA] Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain. [López-Campos G] Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, United Kingdom
dc.identifier.pmid35663898
dc.identifier.wos000806106000001
dc.relation.projectidinfo:eu-repo/grantAgreement/ES/PE2017-2020/RYC2019-026576-I
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess


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