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Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses
Published on: January 20, 2017
Bioinformatics models for predicting antigenic variants of influenza A/H3N2 virus
Yu-Chieh Liao1, Min-Shi Lee, Chin-Yu Ko
1Division of Biostatistics and Bioinformatics, National Health Research Institutes, Zhunan 350, Taiwan.
Motivation:
Continual and accumulated mutations in hemagglutinin (HA) protein of influenza A virus generate novel antigenic strains that cause annual epidemics.
Results:
We propose a model by incorporating scoring and regression methods to predict antigenic variants. Based on collected sequences of influenza A/H3N2 viruses isolated between 1971 and 2002, our model can be used to accurately predict the antigenic variants in 1999-2004 (agreement rate = 91.67%). Twenty amino acid positions identified in our model contribute significantly to antigenic difference and are potential immunodominant positions.
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