Machine Learning Decodes the Microbiome: Advancing Diagnostic Accuracy in Gastrointestinal Diseases

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UDC.coleccionInvestigación
UDC.conferenceTitleISM World Congress on Targeting Microbiota 2024
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.departamentoBioloxía
UDC.grupoInvLaboratorio de Aprendizaxe Automático en Ciencias Vivas (MALL)
UDC.grupoInvLaboratorio de Bases de Datos (LBD)
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación
UDC.institutoCentroINIBIC - Instituto de Investigacións Biomédicas de A Coruña
dc.contributor.authorWasim, Ayesha
dc.contributor.authorMartin-De Arribas, E.
dc.contributor.authorLadra, Susana
dc.contributor.authorPoza, Margarita
dc.contributor.authorFernández-Lozano, Carlos
dc.date.accessioned2026-09-01T09:36:38Z
dc.date.available2026-09-01T09:36:38Z
dc.date.issued2024
dc.descriptionPresentado en: 11th ISM World Congress on Targeting Microbiota, 14-15 October 2024, Malta Versión aceptada
dc.description.abstract[Abstract]: This study evaluates the diagnostic accuracy of machine learning (ML) models on gut microbiome data for gastrointestinal diseases. By assessing batch correction and profiling strategies, the results show that species-level taxonomic features excel for Crohn's disease, whereas ComBat-seq batch correction improves predictive accuracy in colorectal cancer. Algorithms like Random Forest and XGBoost proved most effective, offering guidance for reproducible non-invasive diagnostics.
dc.description.sponsorshipSpanish Ministry of Science and Innovation with funds from the European Union NextGenerationEU, Horizon 2020 research and innovation programme under a Marie Skłodowska-Curie agreement 101034261 (H2020-MSCA-COFUND), from GAIN and MRR funds, Recovery, Transformation and Resilience Plan (PRTRC17.I1), NextGenerationEU (DATAMICROCCR); MCIN/AEI/10.13039/501100011033, GAIN/Xunta de Galicia (GRC: ED431C 2021/53). The present study received funding from the Instituto de Salud Carlos III (ISCIII), Spain, through the projects PI20/00413 and PI23/00696, cofounded by the European Union (EU) to MP.
dc.description.sponsorshipXunta de Galicia; ED431C 2021/53
dc.identifier.citationA. Wasim, E. Martín-de Arribas, S. Ladra, M. Poza, and C. Fernández-Lozano, "Machine Learning Decodes the Microbiome: Advancing Diagnostic Accuracy in Gastrointestinal Diseases" [poster], 11th ISM World Congress on Targeting Microbiota, 14-15 October 2024, Malta
dc.identifier.urihttps://hdl.handle.net/2183/49122
dc.language.isoeng
dc.publisherInternational Society of Microbiota
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101034261
dc.relation.projectIDinfo:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PI20%2F00413/ES/FARMACOMICROBIOMICA Y MEDICINA PERSONALIZADA EN LA TERAPIA DEL CANCER COLORECTAL/
dc.relation.projectIDinfo:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PI23%2F00696/ES/PAPEL DE BACTERIAS ANAEROBIAS EN EL CÁNCER DE COLON Y VALIDACIÓN DE BIOMARCADORES
dc.rights© 2024
dc.rights.accessRightsopen access
dc.subjectMachine learning
dc.subjectGut microbiome
dc.subjectGastrointestinal diseases
dc.titleMachine Learning Decodes the Microbiome: Advancing Diagnostic Accuracy in Gastrointestinal Diseases
dc.typeconference output
dspace.entity.typePublication
relation.isAuthorOfPublication55bfba4e-d15b-4c84-9894-ac53c2278caf
relation.isAuthorOfPublicationa68d08dc-09ba-453e-8928-7c08e5b14a4b
relation.isAuthorOfPublicatione5ddd06a-3e7f-4bf4-9f37-5f1cf3d3430a
relation.isAuthorOfPublication.latestForDiscovery55bfba4e-d15b-4c84-9894-ac53c2278caf

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