Cargando…
Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms
Sepsis, a serious inflammatory response that can be fatal, has a poorly understood pathophysiology. The Metabolic syndrome (MetS), however, is associated with many cardiometabolic risk factors, many of which are highly prevalent in adults. It has been suggested that Sepsis may be associated with Met...
Autores principales: | Tao, Linfeng, Zhu, Yue, Liu, Jun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060809/ https://www.ncbi.nlm.nih.gov/pubmed/37007944 http://dx.doi.org/10.3389/fgene.2023.1129476 |
Ejemplares similares
-
Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms
por: Li, Weimin, et al.
Publicado: (2022) -
Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning
por: She, Han, et al.
Publicado: (2023) -
Novel peripheral blood diagnostic biomarkers screened by machine learning algorithms in ankylosing spondylitis
por: Wen, Jian, et al.
Publicado: (2022) -
Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning
por: Chen, Han, et al.
Publicado: (2023) -
Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning
por: Liu, Jinya, et al.
Publicado: (2022)