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Activity Number:
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393
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Type:
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Invited
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Date/Time:
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Wednesday, August 9, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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WNAR
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| Abstract - #305086 |
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Title:
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Variable Selection in Regression Mixture Modeling for the Discovery of Gene Regulatory Networks
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Author(s):
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Joseph G. Ibrahim*+ and Mayetri Gupta
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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Department of Biostatistics, Chapel Hill, NC, 27599,
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Keywords:
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transcription regulation ; motif discovery ; hierarchical model ; evolutionary Monte Carlo ; importance sampling ; Bayesian model selection
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Abstract:
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The availability of diverse types of genomic data---such as DNA sequence, gene expression microarray, and proteomic data---has led to a rapid growth of statistical research in the effort to decipher gene regulatory networks---the interactions between genes (or groups of genes) in regulating a biological process. A natural way to address these issues is to combine gene clustering and motif discovery in a mixture framework, with unknown components representing the latent gene clusters and genomic sequence features linked to the resultant gene expression through a multivariate hierarchical regression. We demonstrate a hierarchical regression mixture model for genomic sequence and expression data and propose a Monte Carlo method for simultaneous variable selection (motifs) and clustering (genes).
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