Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning
Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning
About this item
Full title
Author / Creator
Publisher
United States: American Society for Microbiology
Journal title
Language
English
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Publication information
Publisher
United States: American Society for Microbiology
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Scope and Contents
Contents
The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models t...
Alternative Titles
Full title
Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_e85ed8bd12404d7b94641691699c9528
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e85ed8bd12404d7b94641691699c9528
Other Identifiers
ISSN
2379-5077
E-ISSN
2379-5077
DOI
10.1128/msystems.00459-22
