AI-driven discovery of minimal sepsis biomarkers for disease detection and progression: precision medicine across diverse populations
Achieving 99.42% accuracy across cohorts, this miniature set outperforms larger published gene sets. These findings provide critical insights for personalized risk assessment, targeted drug development, and tailored treatments for both adult and pediatric sepsis patients.
Authors
- Liying Zhan
- Chengxiu Ling (ORCID: https://orcid.org/0000-0003-3813-0428)
- Yunlong Zhang (ORCID: https://orcid.org/0000-0002-6227-6956)
- Zhou Liu (ORCID: https://orcid.org/0000-0002-5698-3945)
- Zhengjun Zhang (ORCID: https://orcid.org/0000-0003-2615-1539)
- Hanwen Su (ORCID: https://orcid.org/0000-0003-3560-3162)
- Qin Su (ORCID: https://orcid.org/0009-0006-8389-7694)
- Chunming Zhang (ORCID: https://orcid.org/0000-0003-4801-4543)
- Jingtao Huang (ORCID: https://orcid.org/0009-0005-4462-758X)
- Zhihua Lv
Institutions
- University of Wisconsin–Madison (US)
- Wuhan University (CN)
- Renmin Hospital of Wuhan University (CN)
- Institute for Forecasting of the Slovak Academy of Sciences (SK)
- University of Chinese Academy of Sciences (CN)
- Xi’an Jiaotong-Liverpool University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-07-01
- DOI
- https://doi.org/10.3389/fmed.2025.1521827
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hubei Province
- Wuhan University
- Renmin Hospital of Wuhan University