This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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295
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #306713 |
Title:
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Incorporating Nonlinear Relationships in Microarray Missing Value Imputation
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Author(s):
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Hesen Peng*+ and Tianwei Yu and Wei Sun
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Companies:
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Emory University and Emory University and The University of North Carolina at Chapel Hill
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Address:
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1518 Clifton Rd NE , Atlanta, GA, 30322,
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Keywords:
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high-dimensional data ;
missing value imputation
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Abstract:
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Microarray gene expression data often contain missing values. Accurate estimation of the missing values is important for down-stream data analyses that require complete data. Nonlinear relationships between gene expression levels have not been well-utilized in missing value imputatiosn. We propose an imputation scheme based on nonlinear dependencies between genes. By imulations based on real microarray data, we show that incorporating non-linear relationships could improve the accuracy of missing value imputation, both in terms of normalized root mean squared error and in terms of the preservation of the list of significant genes in statistical testing. In addition, we studied the impact of artificial dependencies introduced by data normalization on the simulation results.
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