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Abstract Details
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Activity Number:
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383
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304257 |
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Title:
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Unified Method for Uncovering Subgroups and Identifying Markers with Differential Associations Across Subgroups
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Author(s):
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Mahlet Tadesse*+ and Emanuele Mazzola and Giovanni Parmigiani
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Companies:
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Georgetown University and Dana-Farber Cancer Institute and Harvard School of Public Health
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Address:
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Department of Mathematics and Statistics, Washington, DC, 20057, United States
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Keywords:
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Bayesian inference ;
CART ;
variable selection ;
genomics
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Abstract:
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When evaluating relationships between markers and phenotypes, variables may be positively associated with the outcome in some samples but may have negligible or negative associations among others. We combine clustering and decision tree methods to identify markers with heterogeneous associations across subgroups of samples, where the subgroups themselves are uncovered using a subset of variables. We illustrate the method with applications to genomic studies.
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