Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes.

ABSTRACT: 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 co

SUBMITTER: Linares-Blanco J 

PROVIDER: S-EPMC9157387 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature