“School of Biological Sciences”
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| Paper IPM / Biological Sciences / 15399 |
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High-dimensional time-course gene expression data refer to time course data with a large number of covariates. In this status, variable
selection is a popular approach for selecting important variables. In this paper, we review penalized likelihood mixed effects model for variable
selection in high-dimensional time-course data. Then, the approach is used for variable selection in yeast cell-cycle gene expression data
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