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Abstract Details
Activity Number:
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380
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Asociacion Mexicana de Estadistica
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Abstract - #303724 |
Title:
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Bayesian Analysis of Functional Proteomics Profiles
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Author(s):
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Luis Enrique Nieto-Barajas*+
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Companies:
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Instituto Tecnológico Autónomo de México
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Address:
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Rio Hondo No. 1, Progreso Tizapan, Mexico, D.F. , International, 01080, Mexico
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Keywords:
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Bayesian nonparametrics ;
dependent random measures ;
mixed effects models ;
conditionally autoregressive processes ;
time series analysis
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Abstract:
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Using a new type of array technology, the reverse phase protein array (RPPA), we measure time-course protein expression for a set of selected markers that are known to co-regulate biological functions in a pathway structure. To accommodate the complex dependent nature of the data, including temporal correlation and pathway dependence for the protein markers, we propose a mixed effects model with temporal and protein-specific components. We develop a sequence of random probability measures (RPM) to account for the dependence in time of the protein expression measurements. We also acknowledge the pathway dependence among proteins via a conditionally autoregressive (CAR) model. Applying our model to the RPPA data, we reveal a pathway-dependent functional profile for the set of proteins as well as marginal expression profiles over time for individual markers.
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Authors who are presenting talks have a * after their name.
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