Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.

ABSTRACT:

Background and aims

Ulcerative colitis (UC) is a metabolism-related chronic intestinal inflammatory disease. Disease extent is a key parameter of UC. Using serum metabolic profiling to identify noninvasive biomarkers of disease extent may inform therapeutic decisions and risk stratification.

Methods

The orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to identify the metabolites. Least absolute shrinkage and selection operator regression, random forest-recursive feature elimination, and support vector machine-recursive feature elimination algorithms were used to screen metabolites. Five machine learning algorithms (eXtreme Gradient Boosting, K-NearestNeighbor, Naive Bayes, random forest [RF], and SVM) were used to construct the prediction model.

SUBMITTER: Ge C 

PROVIDER: S-EPMC11829215 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature