Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine-Reference-Cited by-同舟云学术

Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine

Published:2021-04-28 Issue:2 Volume:6 Page:
ISSN:2379-5042
Container-title:mSphere
language:en
Short-container-title:mSphere

Author:

Zeller Michael A.¹²^ORCID,Gauger Phillip C.¹^ORCID,Arendsee Zebulun W.³^ORCID,Souza Carine K.³,Vincent Amy L.³^ORCID,Anderson Tavis K.³^ORCID

Affiliation:

1. Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Iowa, USA

2. Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA

3. Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, Iowa, USA

Abstract

Influenza A viruses (IAV) in swine constitute a major economic burden to an important global agricultural sector, impact food security, and are a public health threat. Despite significant improvement in surveillance for IAV in swine over the past 10 years, sequence data have not been integrated into a systematic vaccine strain selection process for predicting antigenic phenotype and identifying determinants of antigenic drift.

Funder

Iowa State University Presidential Interdisciplinary Research Initiative

Iowa State University Veterinary Diagnostic Laboratory

DOD | Defense Advanced Research Projects Agency

HHS | NIH | National Institute of Allergy and Infectious Diseases

U.S. Department of Energy

USDA | Agricultural Research Service

Publisher

American Society for Microbiology

Subject

Molecular Biology,Microbiology

Link

https://journals.asm.org/doi/pdf/10.1128/mSphere.00920-20

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