Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning.

ABSTRACT: Pediatric Crohn's disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able

SUBMITTER: Chen KA 

PROVIDER: S-EPMC10834600 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature