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Activity Number:
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59
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #310074 |
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Title:
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Using Evolutionary Relationships to Model Correlation in Mixed Effects Models
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Author(s):
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Hua Guo*+ and Robert E. Weiss and Marc A. Suchard
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Companies:
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University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles
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Address:
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3320 Sawtelle Blvd. apt 210, Los Angeles, CA, 90066,
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Keywords:
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Bayesian ; Evolution ; MCMC
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Abstract:
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Studies of gene expression profiles in response to external perturbation generate repeated measures data that generally follow non-linear curves. To explore the evolution of such profiles across a gene family, we introduce phylogenetic repeated measures (PR) models. These models draw strength from two forms of correlation in the data. Through gene duplication, the family's evolutionary relatedness induces the first form. The second is the correlation across time-points. We borrow a Brownian diffusion process along a given phylogenetic tree to account for the relatedness and co-opt a repeated measures framework to model the latter. We analyze the evolution of gene expression in the yeast kinase family using splines to estimate non-linear behavior across three perturbation experiments. PR models outperform previous approaches and afford the prediction of ancestral expression profiles.
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