Understanding influenza-virus host interaction using machine learning to improve food safety
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This dissertation explores the application of machine learning to understand and predict influenza virus phenotypes. Chapter 1 provides background on influenza viruses, their evolution through mutation and reassortment, and the importance of accurate prediction of antigenicity and reassortment for vaccine development. Chapter 2 introduces MAIVeSS, a machine-learning framework for selecting antigenically matched, high-yield vaccine strains. In summary, these chapters demonstrate the use of computational methods to enhance our understanding of influenza virus biology and improve public health responses.
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Ph. D.