4.6 Article

Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes

期刊

FRONTIERS IN MICROBIOLOGY
卷 13, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2022.872671

关键词

machine learning; feature selection; inflammatory bowel disease; microbiome; Crohn's disease; ulcerative colitis

资金

  1. Collaborative 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]
  2. European Regional Development Funds (FEDER)-A way to build Europe
  3. Ramon y Cajal grant [RYC2019-026576-I]
  4. Ministry of Science and Innovation of the Spanish government
  5. Biotechnology and Biological Sciences Research Council (BBSRC) [BB/S006281/1]
  6. Queen's University of Belfast UKRI block grant
  7. BBSRC [BB/S006281/1] Funding Source: UKRI

向作者/读者索取更多资源

This study generated a metagenomic signature with predictive capacity to identify inflammatory bowel disease (IBD) from fecal samples. Different machine learning models were trained and the identified signature showed high performance in both subtypes of IBD. The study also identified different genera that play an important and common role in the development of these two 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 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.

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