Author Correction: UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data
In the version of the article initially published, in the "Model construction process" section of the Methods, the text "we employed machine learning algorithms such as logistic regression, boosting, and deep learning to predict over 18500 Phecodes as a multi-label classification problem" should have read "we employed machine learning algorithms such as logistic regression, boosting, and deep learning to predict over 1560 Phecodes as a multi-label classification problem".
Authors
- Borui Tang
- Z. Zhu
- Y. R. Shen (ORCID: https://orcid.org/0000-0001-8023-2765)
- Xueqin Wang (ORCID: https://orcid.org/0000-0001-5205-9950)
- Zheng Wang (ORCID: https://orcid.org/0000-0001-6157-0662)
- Yukang Jiang (ORCID: https://orcid.org/0000-0001-8274-4676)
- Bingxin Zhao
- Yi-Xiang Wang (ORCID: https://orcid.org/0000-0002-5246-5497)
- Huiyang Peng
- Xiaopu Wang
- Zhiwen Jiang
- Zidan Luo
- Jieping Ye
Institutions
- University of North Carolina at Chapel Hill (US)
- University of Science and Technology of China (CN)
- Alibaba Group (China) (CN)
- University of Pennsylvania (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-01-28
- DOI
- https://doi.org/10.1038/s41467-026-68888-1
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00