Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework

Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework

BackgroundSepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcriptomics data has recently emerged as a valuable resou...

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Bibliographic Details
Main Authors: Chao Du, Stephanie C. Tan, Heng-Fu Bu, Saravanan Subramanian, Hua Geng, Xiao Wang, Hehuang Xie, Xiaowei Wu, Tingfa Zhou, Ruijin Liu, Zhen Xu, Bing Liu, Xiao-Di Tan
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-11-01
Series:Frontiers in Immunology
Subjects:
sepsis
septic shock
biomarkers
machine learning for disease diagnosis
translational medicine
SepxFindeR model
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2024.1493895/full
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https://www.frontiersin.org/articles/10.3389/fimmu.2024.1493895/full

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