A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics

Early risk assessments and interventions for metabolic syndrome (MetS) are limited because of a lack of effective biomarkers. In the present study, several candidate genes were selected as a blood-based transcriptomic signature for MetS. We collected so far the largest MetS-associated peripheral blo...

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Detalles Bibliográficos
Autores principales: Liu, Guanzhi, Luo, Sen, Lei, Yutian, Wu, Jianhua, Huang, Zhuo, Wang, Kunzheng, Yang, Pei, Huang, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2021
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806918/
https://www.ncbi.nlm.nih.gov/pubmed/34516309
http://dx.doi.org/10.1080/21655979.2021.1968249

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806918/
https://www.ncbi.nlm.nih.gov/pubmed/34516309
http://dx.doi.org/10.1080/21655979.2021.1968249

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