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Activity Number:
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59
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309958 |
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Title:
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A Bayesian Integrated Approach for Learning About Renal Clear
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Author(s):
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David Gold*+ and Loleta Harris and Kevin Coombes and Bani Mallick
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Companies:
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Texas A&M University and The University of Texas M.D. Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center and Texas A&M University
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Address:
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400 Nalge St Apt 406, College Station, TX, 77840,
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Keywords:
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microarrays ; renal carcinoma ; bayesian ; hierarchical modeling ; gene enrichment
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Abstract:
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Renal clear cell carcinoma (RCC) is a deadly and complex disease. Attempts to combat RCC would benefit greatly from improvements to the list of candidate genes associated with the disease. Many past microarray studies have failed to identify effective targets for treatment, although more promising results were shown by Lenburg et al. (2003), who compared normal renal to renal tumor gene expression on Affymetrix U133 chips. Identifying effective targets for treatment in high-throughput experiments such as Lenburg et al.'s microarray study is typically complicated by the uncertainty in the gene regulatory networks, i.e. gene interactions, responsible for cancer. We perform multivariate gene inference on the Lenburg et al. microarray data set with a fully Bayesian approach, Bayesian Learning for Microarrays (BLM), with prior information of gene classes.
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