Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes
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AuthorLiñares Blanco, José
Machine learningFeature selectionInflammatory bowel diseaseMicrobiomeChron's diseaseUlcerative colitis
Liñares-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]
SponsorshipCollaborative Project in Genomic Data Integration (CICLOGEN) - Carlos III Health Institute from the Spanish; National plan for Scientific and Technical Research and Innovation 2013-2016 PI17/01826; European Regional Development Funds (FEDER)-A way to build Europe; Spanish Government RYC2019-026576-I; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC) BB/S006281/1; Queen's University of Belfast UKRI block grant
Inflammatory 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.