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Paper   IPM / Biological Sciences / 15399
School of Biological Sciences
  Title:   Application of Penalized Mixed Model in Identification of Genes in Yeast Cell-Cycle Gene Expression Data
  Author(s): 
1.  Mojtaba Ganjali
2.  Taban Baghfalaki
  Status:   Published
  Journal: Biostatistics and Biometrics
  No.:  2
  Vol.:  6
  Year:  2018
  Supported by:  IPM
  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

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