Identifying metabolic shifts in Crohn's disease using' omics-driven contextualized computational metabolic network models.

ABSTRACT: Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven me

SUBMITTER: Fernandes P 

PROVIDER: S-EPMC9814625 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature