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

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 28, 2021
Source ID
10.1128/msphere.00920-20

Entities

People

  • Amy L Vincent
  • Carine K. Souza
  • Michael A. Zeller
  • Phillip C Gauger
  • Tavis K Anderson
  • Zebulun Arendsee

Organizations

  • Agricultural Research Service
  • Iowa State University
  • National Institute of Allergy and Infectious Diseases
  • United States Department of Energy

Tags

Readers

  • Computational Modeling and Simulation
  • Infectious Disease/Epidemiology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Biotechnology