ArticleExperimental and therapeutic medicine2025
Identification of amino acid metabolism‑related genes as diagnostic and prognostic biomarkers in sepsis through machine learning.
Article in Experimental and therapeutic medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Identification of immune cell mitochondrial dysfunction characteristics and clinical predictive biomarkers in sepsis via multi-cohort machine learning and single-cell RNA sequencing.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
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3 authors.
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Abstract
Previous research has highlighted the critical role of amino acid metabolism (AAM) in the pathophysiology of sepsis. The present study aimed to explore the potential diagnostic and prognostic value of AAM-related genes (AAMGs) in sepsis, as well as their underlying molecular mechanisms. Gene expression profiles from the Gene Expression Omnibus (GSE65682, GSE185263 and GSE154918 datasets) were analyzed. Based on weighted gene co-expression network analysis and machine learning algorithms, hub AAMGs were identified in the GSE65682 database. Subsequently, hub AAMGs were evaluated for their expression levels and diagnostic and prognostic significance in sepsis, as well as their interactions with regulatory pathways and role in immune cell infiltration. Additionally, trends in AAMG expression were validated using clinical samples, and their functions in sepsis were confirmed through an
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