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Activity Number:
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85
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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WNAR
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| Abstract - #307719 |
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Title:
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Bayesian Hierarchical Spatially Correlated Functional Data Analysis with Application to Colon Carcinogenesis
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Author(s):
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Veera Baladandayuthapani*+ and Bani Mallick and Raymond J. Carroll and Mee Young Hong and Nancy D. Turner and Joanne R. Lupton
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Companies:
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The University of Texas M.D. Anderson Cancer Center and Texas A&M University and Texas A&M University and University of California, Los Angeles and Texas A&M University and Texas A&M University
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Address:
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1515 Holcombe Blvd. Unit 447, Houston, TX, 77030,
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Keywords:
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Bayesian methods ; Carcinogenesis ; Functional data analysis ; Regression splines ; Semiparametric methods ; Spatial correlation
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Abstract:
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We present new methods to analyze data from an experiment using rodent models to investigate the biological mechanisms surrounding p27, an important biomarker predictive of early colon carcinogenesis. The responses modeled are essentially functions nested within a two-stage hierarchy. Moreover, in our experiment, there is substantial biological motivation for the existence of spatial correlation among the functions, which arise from the locations of biological structures called colonic crypts: this possible functional correlation is a phenomenon we term crypt signaling. Thus, as a point of general methodology, we require an analysis that allows for functions to be correlated at the deepest level of the hierarchy. Analysis of this data set gives new insights into the structure of p27 expression in early colon carcinogenesis and suggests the existence of significant crypt signaling.
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