Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data

Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data

Abstract Despite the end of the global Coronavirus Disease 2019 (COVID-19) pandemic, the risk factors for COVID-19 severity continue to be a pivotal area of research. Specifically, studying the impact of the genomic diversity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on COVID-1...

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Bibliographic Details
Main Authors: Miao Miao, Yonghong Ma, Jiao Tan, Renjuan Chen, Ke Men
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
COVID-19
SARS-CoV-2
Severity prediction
Risk factor
Machine learning
Interpretability analysis
Online Access:https://doi.org/10.1038/s41598-024-78493-1
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