Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes - Liñares-Blanco, Jose; Fernandez-Lozano, Carlos; Seoane, Jose A.; López-Campos, Guillermo (Frontiers in Microbiology, 2022)
Inflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of role of microorganims in this disease development. Because of 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 obtained a metagenomic signature with predictive capacity according to the presence of IBD in faecal samples. Different Machine Learning models were trained, obtaining high performance measures. The predictive capacity of the signature was validated in two external cohorts. Specifically, in one cohort containing samples from Ulcerative Colitis and one from 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 generalisation 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.