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

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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Bibliographic Details
Main Authors: Guanzhi Liu, Sen Luo, Yutian Lei, Jianhua Wu, Zhuo Huang, Kunzheng Wang, Pei Yang, Xin Huang
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Bioengineered
Subjects:
machine learning
metabolic syndrome
bioinformatics
biomarkers
gene hub
Online Access:http://dx.doi.org/10.1080/21655979.2021.1968249

Internet

http://dx.doi.org/10.1080/21655979.2021.1968249

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