Improving Differentiation of Crohn's Disease and Ulcerative Colitis Proteomes through Protein-Wide Association Study Feature Selection in Machine Learning.

ABSTRACT:

Background and aims

Diagnostic differentiation between Crohn's disease (CD) and ulcerative colitis (UC) is crucial for timely and suitable therapeutic measures. The current gold standard for differentiating between CD and UC involves endoscopy and histology, which are invasive and costly. We aimed to identify blood plasma proteomic signatures using a Protein-Wide Association Study (PWAS) approach to differentiate CD from UC and evaluate the efficacy of these signatures as features in machine learning (ML) classifiers.

Methods

Among participants (n=1,106; n_CD=636; n_UC=470) of the Study of a Prospective Adult Research Cohort with IBD (SPARC), plasma protein (n=2,920) levels were estimated using Olink proteomics. A PWAS with Bonferroni correction for multi

SUBMITTER: Gorelik MG 

PROVIDER: S-EPMC11601736 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature