RT Journal Article
T1 Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes.
A1 Liñares-Blanco, Jose
A1 Fernandez-Lozano, Carlos
A1 Seoane, Jose A
A1 López-Campos, Guillermo
K1 Crohn's disease
K1 feature selection
K1 inflammatory bowel disease
K1 machine learning
K1 microbiome
K1 ulcerative colitis
AB 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.
SN 1664-302X
YR 2022
FD 2022-05-17
LK http://hdl.handle.net/10668/20624
UL http://hdl.handle.net/10668/20624
LA en
DS RISalud
RD Apr 3, 2025