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
- DOI
- 10.1186/s12887-023-03896-4
- Published
- 2023-02-24
- Container
- BMC Pediatrics
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s12887-023-03896-4,
title = {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},
author = {Yaxue Xie and Hongshuo Shi and Bo Han},
year = {2023},
journal = {BMC Pediatrics},
doi = {10.1186/s12887-023-03896-4},
url = {https://doi.org/10.1186/s12887-023-03896-4}
}RIS
TY - JOUR TI - 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 AU - Yaxue Xie AU - Hongshuo Shi AU - Bo Han PY - 2023 JO - BMC Pediatrics DO - 10.1186/s12887-023-03896-4 UR - https://doi.org/10.1186/s12887-023-03896-4 ER -
APA
Xie, Y., Shi, H., & Han, B. (2023). 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. BMC Pediatrics. https://doi.org/10.1186/s12887-023-03896-4
Source records
- crossref · retrieved 2026-09-26T07:23:05.103Z