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Activity Number:
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372
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303962 |
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Title:
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A Bayesian Approach to the Quantification of Protein Lysate Arrays
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Author(s):
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E. Shannon Neeley*+ and C. Shane Reese
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Companies:
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Brigham Young University and Brigham Young University
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Address:
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Department of Statistics, Provo, UT, 84602,
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Keywords:
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Hierarchical models ; Protein arrays ; Random Curves
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Abstract:
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Because proteins perform essential roles in many biological processes, the quantification of protein expression and post translation modifications can provide insight to many molecular systems, including disease progression and identification. Reverse-phase protein lysate arrays measure the relative expression of one protein in many cellular samples simultaneously on the same array. Current parametric quantification methods fit a sigmoid model to dilution series data. We develop a Bayesian hierarchical nonlinear model to quantify protein lysate arrays based on a versatile class of growth curves that includes the sigmoid model as a subset. The hierarchical model allows us to estimate unique relative protein expressions for each sample on the array. This richer class of models enables better estimation of growth curve data, even when sigmoid distributional assumptions are not met.
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