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Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes
Inflammatory 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 t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9157387/ https://www.ncbi.nlm.nih.gov/pubmed/35663898 http://dx.doi.org/10.3389/fmicb.2022.872671 |
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author | Liñares-Blanco, Jose Fernandez-Lozano, Carlos Seoane, Jose A. López-Campos, Guillermo |
author_facet | Liñares-Blanco, Jose Fernandez-Lozano, Carlos Seoane, Jose A. López-Campos, Guillermo |
author_sort | Liñares-Blanco, Jose |
collection | PubMed |
description | Inflammatory 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. |
format | Online Article Text |
id | pubmed-9157387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91573872022-06-02 Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes Liñares-Blanco, Jose Fernandez-Lozano, Carlos Seoane, Jose A. López-Campos, Guillermo Front Microbiol Microbiology Inflammatory 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. Frontiers Media S.A. 2022-05-17 /pmc/articles/PMC9157387/ /pubmed/35663898 http://dx.doi.org/10.3389/fmicb.2022.872671 Text en Copyright © 2022 Liñares-Blanco, Fernandez-Lozano, Seoane and López-Campos. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Liñares-Blanco, Jose Fernandez-Lozano, Carlos Seoane, Jose A. López-Campos, Guillermo Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes |
title | Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes |
title_full | Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes |
title_fullStr | Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes |
title_full_unstemmed | Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes |
title_short | Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes |
title_sort | machine learning based microbiome signature to predict inflammatory bowel disease subtypes |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9157387/ https://www.ncbi.nlm.nih.gov/pubmed/35663898 http://dx.doi.org/10.3389/fmicb.2022.872671 |
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