Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis

• Published in: Clinical and experimental pharmacology & physiology Vol. 52; no. 7; pp. e70031 - n/a
• Main Authors: Li, Ruichen, Wang, Qiushi, Gao, Ru, Shen, Rutao, Wang, Qihao, Cui, Xiuliang, Jiang, Zhiming, Zhang, Lijie, Fang, Jingjing
• Format: Journal Article
• Language: English
• Published: Australia Wiley Subscription Services, Inc 01.07.2025
• Subjects: 1-Phosphatidylinositol 3-kinase; AKT protein; Artificial intelligence; Biomarkers; Curcumin; Deep Learning; Explainable artificial intelligence; Gene Expression Profiling - methods; Gene Expression Regulation; Genes; Humans; immune infiltration analysis; Immune response; Immune system; Immunoregulation; molecular docking; Necroptosis; Rotenone; Sepsis; Sepsis - genetics; Sepsis - immunology; Signal transduction; Signal Transduction - genetics; Tissue inhibitor of metalloproteinase 1; Tissue Inhibitor of Metalloproteinase-1 - genetics; Transcriptome - genetics; Transcriptomics; explainable artificial intelligence; molecular docking; biomarkers; immune infiltration analysis; sepsis

ABSTRACT Sepsis is a life‐threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the immune response in sepsis. In this study, we used P‐NET, a biologically informed explainable artificial intelligence model, to evaluate the g...