Proteomics‐enabled learning machine algorithms enhance the prediction of cardiovascular diseases in patients with type 2 diabetes mellitus
Proteomics can be used to predict cardiovascular complications in diabetic patients. It is also necessary to consider the applicability of the model due to the limitations of the sample size and the constraints of proteomics in clinical applications.
Authors
- Xiao Tan (ORCID: https://orcid.org/0000-0003-3992-5812)
- Yuefeng Yu (ORCID: https://orcid.org/0000-0001-7439-4231)
- Bowei Yu (ORCID: https://orcid.org/0009-0001-7056-400X)
- Ningjian Wang (ORCID: https://orcid.org/0000-0001-9591-6991)
- Yingli Lu (ORCID: https://orcid.org/0000-0002-5117-1614)
- Bin Wang (ORCID: https://orcid.org/0000-0002-4869-1352)
- Jiang Li (ORCID: https://orcid.org/0000-0001-5596-9159)
- Lijie Liu (ORCID: https://orcid.org/0000-0002-8309-609X)
- Ying Sun (ORCID: https://orcid.org/0000-0002-4947-9783)
- Yuying Wang (ORCID: https://orcid.org/0000-0003-1072-6633)
Institutions
- Uppsala University (SE)
- Chinese University of Hong Kong (HK)
- Shanghai Jiao Tong University (CN)
- Shanghai Ninth People's Hospital (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Diabetes Obesity and Metabolism
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1111/dom.70064
- Citations
- 2
- Primary Topic
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Type
- article
- Field-Weighted Citation Impact
- 2.59
Funders
- Shanghai Municipal Health Commission
- National Natural Science Foundation of China