Identification of key genes and development of an identifying machine learning model for sepsis

• Published in: Inflammation research Vol. 74; no. 1; p. 100
• Main Authors: Li, Zhonghao, Chen, Shengsong, Gao, Nan, Chen, Jie, Qin, Ying, Zhang, Guoqiang
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2025; Springer Nature B.V
• Subjects: Allergology; Animals; Approximation; Biomedical and Life Sciences; Biomedicine; Databases, Genetic; Datasets; Dermatology; DQA1 protein; Fibroblasts; Gene expression; Gene Expression Profiling; Gene sequencing; Genes; Genomics; Heart; Histocompatibility antigen HLA; Humans; Identification; Immunology; Learning algorithms; Lung - metabolism; Lungs; Machine Learning; Mice; Neurology; Pathology; Pharmacology/Toxicology; Rheumatology; Sepsis; Sepsis - diagnosis; Sepsis - genetics; Sequence Analysis, RNA; Single-Cell Analysis; Support Vector Machine; Support vector machines; Computer biology; Immunology; Bioinformation; Pyemia; Translational medicine
• ISSN: 1023-3830; 1420-908X; 1420-908X
• DOI: 10.1007/s00011-025-02068-7

Objective and design This study aims to identify key genes of sepsis and construct a model for sepsis identification through integrated multi-organ single-cell RNA sequencing (scRNA-seq) and machine learning. Material or subjects Datasets downloaded from the Gene Expression Omnibus (GSE207363, GSE20...