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Activity Number:
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450
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Type:
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Invited
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Date/Time:
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Wednesday, August 9, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #305150 |
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Title:
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A Bayesian Hierarchical Model for Integrating Biological Data
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Author(s):
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Shane Jensen*+
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Companies:
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The Wharton School of the University of Pennsylvania
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Address:
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Department of Statistics, 463 Jon M. Huntsman Hall, Philadelphia, PA, 19104,
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Keywords:
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hierarchical models ; Bayesian framework ; gene expression ; chip binding ; clustering ; promotor elements
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Abstract:
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A substantial focus of research in molecular biology is the network of factors which control the involvement of different genes in living cells. Previous statistical approaches for identifying gene regulatory networks have used gene expression data, ChIP binding data or promoter sequence data, but each of these resources provides only partial information. We present a Bayesian hierarchical model that integrates all three data types in a principled fashion. The gene expression data is modeled as a function of the unknown gene regulatory network which has an informed prior distribution based upon both ChIP binding and promoter sequence data. In this context, we discuss procedures for balancing multiple sources of prior information. Applications to both yeast and human data are presented and validated using several external sources of information.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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