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Abstract Details
Activity Number:
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467
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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Abstract - #302211 |
Title:
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Identification of Significant Features in High-Dimensional Data Using Supervised Principal Components
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Author(s):
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Flora Mulkey*+ and Eric Bair
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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10 Red Bluff Ct. , Durham, NC, 27713,
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Keywords:
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Feature Selection ;
Microarray ;
High-Dimensional Data ;
Semi-Supervised Methods ;
Genetics
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Abstract:
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We consider the problem of identifying features that are associated with an outcome of interest in high-dimensional data. This problem is particularly relevant in the analysis of DNA microarray data and genome-wide association data, where measurements from thousands of genes can be evaluated to further elucidate the genetic causes of complex diseases. We show that a method called supervised principal components can successfully identify features associated with a specific outcome more accurately than conventional methods. It is particularly effective in situations where the outcome variable is measured with a high degree of error or when the outcome variable is merely a surrogate for an unobserved variable. For example, the survival time of cancer patients is frequently used as a surrogate variable for unobserved tumor subtypes. We apply our procedure to a series of simulated and real genetic data sets and show that it outperforms conventional methods and several competing approaches.
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