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Activity Number:
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296
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #303069 |
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Title:
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Modeling Dependent Gene Expression
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Author(s):
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Peter Müller*+ and Donatello Telesca and Giovanni Parmigiani
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Companies:
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The University of Texas M.D. Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center and Johns Hopkins University
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Address:
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Department of Biostatistics, Houston, TX, 77030,
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Keywords:
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conditional independence ; microarray data ; graphical models
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Abstract:
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We consider statistical inference for high throughput gene expression data. Most traditional statistical methods implicitly assume independent sampling (conditional on some hyperparameters). Recognizing the limitations of independent modeling we develop a model that includes a simple dependence structure across genes. The important features of the proposed model are the ease of representing typical prior information on the nature of dependencies, model-based parsimonious representation of the signal as a ordinal outcome, and the use of a coherent probability model over both, structure and strength of the conjectured dependencies. As part of the inference we reduce the recorded data to a trinary response representing underexpression, average expression and overexpression. To achieve this, we use an extension of a model proposed in recent literature.
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