Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning
Research article
Published on
Journal: Computers in biology and medicine [Category] COVID19(2023년), SARS, 변종, 유전자 메커니즘,
Journal: Computers in biology and medicine [Category] COVID19(2023년), SARS, 변종, 유전자 메커니즘,
[키워드] age and sex
attribute
bioinformatics
chronic disease
Comorbidity
complex
country
COVID-19
COVID-19 disease severity
data extraction
Delta
demonstrated
Effect
effort
fixed
GISAID
heterogeneous
include
information
Interpretation
lack
logistic regression models
machine learning
Meta-analysis
Metadata
Mild
Mutation
obesity
omicron
outcome
pandemic
Patient
Predictive
random
random effect
reduction in
Repository
risk
robust
SARS-CoV-2
SARS-CoV-2 sequence
SARS-CoV-2 spike protein
sequence
severe disease
severity
Spike protein
submitted
subset
time-consuming
training
vaccination
variant
viral genome sequence
viral genomics
virus
while
[DOI] 10.1016/j.compbiomed.2022.105969 [Article Type] Research article
[DOI] 10.1016/j.compbiomed.2022.105969 [Article Type] Research article