Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis

In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functionally related ALI-associated hub genes in sepsis...

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Veröffentlicht in:	International journal of biological macromolecules 2024-12, Vol.282 (Pt 3), p.136961, Article 136961
Hauptverfasser:	Sun, Keyu, Wu, Fupeng, Zheng, Jiayi, Wang, Han, Li, Haidong, Xie, Zichen
Format:	Artikel
Sprache:	eng
Schlagworte:	Acute Lung Injury - blood Acute Lung Injury - etiology Acute Lung Injury - genetics Acute Lung Injury - pathology Animals bioinformatics Biomarkers - blood blood Blood markers cardiovascular diseases Computational Biology - methods data collection Disease Progression gene expression Gene Expression Profiling Humans intercellular adhesion molecule-1 lungs Machine Learning Machine learning model Male metabolism Mice Molecular signature RNA RNA-Seq Sepsis - blood Sepsis - complications Sepsis - genetics sepsis-induced acute lung injury Single-Cell Analysis Single-Cell Gene Expression Analysis Transcriptome
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