This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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609
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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Abstract - #308032 |
Title:
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Bayesian Joint Modeling of Multiple Gene Networks and Diverse Genomic Data to Identify Target Genes of a Transcription Factor
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Author(s):
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Peng Wei*+ and Wei Pan
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Companies:
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The University of Texas School of Public Health and University of Minnesota
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Address:
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1200 Pressler Dr., Houston, TX, 77030,
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Keywords:
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Bayesian hierarchical modeling ;
gene networks ;
joint modeling ;
Markov random field ;
systems biology
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Abstract:
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We consider integrative modeling of multiple gene networks and diverse genomic data including protein-DNA binding, gene expression and DNA sequence data to accurately identify the regulatory target genes of a transcription factor (TF). Rather than treating all the genes equally and independently a priori in existing joint modeling approaches, we incorporate the biological prior knowledge that neighboring genes on a gene network tend to be (or not to be) regulated by a TF together. To maximize the use of all existing biological knowledge, we allow the incorporation of multiple gene networks into joint modeling of genomic data by introducing a Markov random field-based model. Application to an E. coli dataset together with simulation studies demonstrates the utility and statistical efficiency gains of the proposed joint model.
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