Abstract
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
Highlights
Expression DataHigh-dimensional time-course gene expression data refer to time course data with a large number of covariates
Linear mixed effects models have been used in a variety of study to analyze data with between-subject dependence [1]
We review the ordinary penalized likelihood approach for variable selection in mixed effects model
Summary
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. We review penalized likelihood mixed effects model for variable selection in high-dimensional time-course data. The approach is used for variable selection in yeast cell-cycle gene expression data. Keywords : Gene expression data; Mixed effects model; Penalty function; Penalized likelihood; Time-course data
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