Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework
Our bioinformatic approach led to the discovery of the 6-HubGss biomarker panel and the development of the SepxFindeR machine learning model, enabling accurate prediction of septic shock and distinction from sepsis with rapid processing capabilities.
Authors
- Tingfa Zhou
- Ruijin Liu (ORCID: https://orcid.org/0000-0003-2326-9507)
- Heng‐Fu Bu
- Stephanie Tan (ORCID: https://orcid.org/0000-0001-7829-2064)
- Hehuang Xie (ORCID: https://orcid.org/0000-0001-5739-1653)
- Zhen Xu (ORCID: https://orcid.org/0000-0002-2325-6514)
- Xiaowei Wu (ORCID: https://orcid.org/0000-0001-7267-5371)
- Hua Geng (ORCID: https://orcid.org/0000-0002-0409-2489)
- Saravanan Subramanian (ORCID: https://orcid.org/0000-0003-2502-9531)
- Xiao Wang (ORCID: https://orcid.org/0000-0002-8997-9256)
- Chao Du (ORCID: https://orcid.org/0009-0005-1139-5612)
- Xiao‐Di Tan (ORCID: https://orcid.org/0000-0003-3766-5885)
- Bing Liu (ORCID: https://orcid.org/0000-0002-3938-1539)
Institutions
- Northwestern University (US)
- Weifang Medical University (CN)
- Shandong University (CN)
- Loyola University Chicago (US)
- University of Illinois Chicago (US)
- Virginia–Maryland College of Veterinary Medicine (US)
- Linyi People's Hospital (CN)
- Weihai Municipal Hospital (CN)
- Jesse Brown VA Medical Center (US)
- Virginia Tech (US)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-11-28
- DOI
- https://doi.org/10.3389/fimmu.2024.1493895
- Citations
- 5
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.93
Funders
- Northwestern University
- Feinberg School of Medicine