Stool-Based Proteomic Signature for the Noninvasive Classification of Crohn's Disease and Ulcerative Colitis Using Machine Learning
This study demonstrates the potential of combining multiple stool protein biomarkers through high-throughput data-independent acquisition mass spectrometry and machine learning tools to develop a predictive model for efficiently distinguishing CD from UC.
Authors
- Hugo Gagnon (ORCID: https://orcid.org/0009-0005-9564-9121)
- François‐Michel Boisvert (ORCID: https://orcid.org/0000-0001-8882-8619)
- David Gagné
- Mandy Malick (ORCID: https://orcid.org/0009-0003-3571-7060)
- Elmira Shajari
- Maxime Delisle (ORCID: https://orcid.org/0000-0003-0100-3898)
- Jean‐François Beaulieu (ORCID: https://orcid.org/0000-0002-8977-3629)
- Marie A. Brunet (ORCID: https://orcid.org/0000-0001-5973-3522)
- Patricia Roy
- Jean-François Noël (ORCID: https://orcid.org/0009-0000-6434-1419)
- Francis Bourassa (ORCID: https://orcid.org/0009-0001-9012-7245)
Institutions
- Université de Sherbrooke (CA)
- Centre Hospitalier Universitaire de Sherbrooke (CA)
- NGC Aerospace (Canada) (CA)
Publication Details
- Journal
- Clinical and Translational Gastroenterology
- Published
- 2025-10-02
- DOI
- https://doi.org/10.14309/ctg.0000000000000925
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00