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Activity Number:
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62
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #306519 |
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Title:
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Dynamic Network Analysis of Time-Course Gene Expression Data
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Author(s):
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Donatello Telesca*+ and Lurdes Y. T. Inoue
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Companies:
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University of Washington and University of Washington
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Address:
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Department of Statistics, Seattle, WA, 98195-4322,
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Keywords:
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time-course gene expression ; dynamic networks ; compound processes ; dynamic time warping ; functional similarity ; MCMC
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Abstract:
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Time-course gene expression data consist of RNA expression from a common set of genes, collected at selected time points, usually spanning over the domain of an underlying biological process developing over time. In order to identify gene to gene interactions, we assume that a sample of genes is a realization of a compound process where gene expression profiles over time are modeled as a random functional transformation of a reference curve. We propose measures of functional similarity and time order based on the estimated warping functions. This allows for novel inferences on dynamic network which takes full account of the timing structure of functional features associated with the gene expression profiles. We discuss the application of our model to simulated and time-course microarray data arising from animal models on prostate cancer progression.
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