Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model

Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model

Abstract Background Kawasaki disease (KD) is a febrile systemic vasculitis involvingchildren younger than five years old. However, the specific biomarkers and precise mechanisms of this disease are not fully understood, which can delay the best treatment time, hence, this study aimed to detect the p...

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Bibliographic Details
Main Authors: Yaxue Xie, Hongshuo Shi, Bo Han
Format: Article
Language:English
Published: BMC 2023-02-01
Series:BMC Pediatrics
Subjects:
Kawasaki disease
Weighted gene correlation network analysis
LASSO regression model
ceRNA network
CIBERSORT
Online Access:https://doi.org/10.1186/s12887-023-03896-4

Internet

https://doi.org/10.1186/s12887-023-03896-4

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