Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation
This study demonstrates a highly accurate machine learning model for FSGS diagnosis. Immunohistochemical validation confirmed elevated expression of CD99 and OAZ2, offering valuable insights into FSGS pathogenesis and potential biomarkers for clinical application.
Authors
- Yonggui Wu (ORCID: https://orcid.org/0000-0001-6434-4759)
- Xue‐qi Liu (ORCID: https://orcid.org/0000-0002-5287-2471)
- Yating Ge
- Jinlian Shu
- Jiang Xiao (ORCID: https://orcid.org/0000-0001-5429-2115)
Institutions
- Anhui Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Second Affiliated Hospital of Anhui Medical University (CN)
- Third People's Hospital of Hefei (CN)
Publication Details
- Journal
- International Journal of General Medicine
- Published
- 2025-02-01
- DOI
- https://doi.org/10.2147/ijgm.s498407
- Primary Topic
- Renal Diseases and Glomerulopathies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Anhui Medical University