Machine learning-based unsupervised phenotypic clustering analysis of patients with IgA nephropathy: Distinct therapeutic responses of different groups
The unsupervised clustering method provided reliable classification of IgAN patients into different subgroups according to clinical features, prognoses, and treatment responsiveness. Our subgroup-based prediction model has significant clinical utility for the assessment of risk and treatment in patients with IgAN.
Authors
- Xingji Lian (ORCID: https://orcid.org/0000-0001-8378-1292)
- Guixian Wu (ORCID: https://orcid.org/0000-0003-4339-2633)
- Qian Zhou (ORCID: https://orcid.org/0000-0003-3954-5449)
- Qiong Wen (ORCID: https://orcid.org/0000-0002-0517-8460)
- Chao Chen (ORCID: https://orcid.org/0000-0003-2640-6415)
- Lingling Liu (ORCID: https://orcid.org/0000-0002-0676-5202)
- Cheng Wang (ORCID: https://orcid.org/0000-0002-1590-7954)
- Yiqin Wang (ORCID: https://orcid.org/0000-0002-0280-7495)
- Wei Chen (ORCID: https://orcid.org/0000-0001-7194-8040)
- Yunfang Zhang (ORCID: https://orcid.org/0000-0002-9966-5023)
- Qinghua Liu (ORCID: https://orcid.org/0000-0002-6385-5482)
Institutions
- Sun Yat-sen University (CN)
- Fifth Affiliated Hospital of Sun Yat-sen University (CN)
- Guangzhou First People's Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Southern Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Chinese Medical Journal
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1097/cm9.0000000000003422
- Citations
- 3
- Primary Topic
- Renal Diseases and Glomerulopathies
- Type
- article
- Field-Weighted Citation Impact
- 4.29
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province
- National Health Commission of the People's Republic of China