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Activity Number:
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594
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305153 |
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Title:
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Bayesian Hierarchical Modeling Using an Iterative Reweighting Algorithm Within Gibbs: An Analysis of Individual Posterior Distributions of Phylogenetic Influenza Data
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Author(s):
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Jennifer Tom*+
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Companies:
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University of California, Los Angeles
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Address:
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, Los Angeles, CA, 90025,
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Keywords:
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Bayesian Hierarchical Modeling ; Iterative Reweighting Algorithm ; Phylogenetics
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Abstract:
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Despite the innate human immune response and annually reengineered vaccine, the influenza A virus continues to evade eradication using a number of evolutionary mechanisms. The influenza A genome has eight segments. During reassortment, two subtypes co-infect a single host cell and exchange segments. Using Bayesian hierarchical modeling and an iterative reweighting algorithm, we analyzed correlated posterior distributions of key phylogenetic parameters in order to study the evolution of the complete flu genome through time, subtype and space. This study focused on the following questions: (1) Are reassortment events coincident with shifts in HA antigen city? (2) Do certain segments maintain greater genetic diversity? (3) Are the genetic histories of certain segments correlated?
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