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• W1594469075 abstract "We propose a method for detecting differential gene expression that exploits the correlation between genes. Our proposal averages the univariate scores of each feature with the scores in correlation neighborhoods. In a number of real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also provide some analysis of the asymptotic behavior of our proposal. The general idea of correlation-sharing can be applied to other prediction problems involving a large number of correlated features. We give an example in protein mass spectrometry." @default.
• W1594469075 created "2016-06-24" @default.
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• W1594469075 date "2006-08-02" @default.
• W1594469075 modified "2023-09-27" @default.
• W1594469075 title "Correlation-sharing for detection of differential gene expression" @default.
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