Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis
Our findings revealed the metabolic landscape of UC and identified biomarkers for different disease extents, confirming the value of metabolites in predicting the occurrence and progression of UC.
Authors
- Hong Shen (ORCID: https://orcid.org/0000-0003-1878-7334)
- Yijing Liu (ORCID: https://orcid.org/0000-0002-7503-3910)
- Changchang Ge (ORCID: https://orcid.org/0000-0002-3044-4820)
- Zhaofeng Shen (ORCID: https://orcid.org/0000-0002-7914-3965)
- Xiaojuan Liu (ORCID: https://orcid.org/0000-0003-2435-5837)
- Yi Lü (ORCID: https://orcid.org/0000-0001-7614-6661)
- Mengyuan Zhang (ORCID: https://orcid.org/0000-0002-6724-9658)
- Lei Zhu (ORCID: https://orcid.org/0000-0001-6033-5775)
- Yizhou Lu (ORCID: https://orcid.org/0000-0003-1399-8079)
Institutions
- Nanjing University of Chinese Medicine (CN)
Publication Details
- Journal
- Journal of Crohn s and Colitis
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1093/ecco-jcc/jjaf020
- Citations
- 13
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 10.48
Funders
- Government of Jiangsu Province
- National Key Research and Development Program of China