Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches

Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with cont...

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Bibliographic Details
Main Authors: Qi Wang, Zhenzhong Su, Jing Zhang, He Yan, Jie Zhang
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Human Vaccines & Immunotherapeutics
Subjects:
COVID-19
differential expression genes
bioinformatics
immune infiltration
WGCNA
machine learning models
Online Access:https://www.tandfonline.com/doi/10.1080/21645515.2024.2310359
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https://www.tandfonline.com/doi/10.1080/21645515.2024.2310359

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