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Application of Penalized Mixed Model in Identification of Genes in Yeast Cell-Cycle Gene Expression Data

Author

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  • Mojtaba Ganjali

    (Department of Statistics, Shahid Beheshti University, Iran
    School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Iran)

  • Taban Baghfalaki

    (Department of Statistics, Tarbiat Modares University, Iran
    School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Iran)

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

Suggested Citation

  • Mojtaba Ganjali & Taban Baghfalaki, 2018. "Application of Penalized Mixed Model in Identification of Genes in Yeast Cell-Cycle Gene Expression Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(2), pages 38-41, April.
    • Handle: RePEc:adp:jbboaj:v:6:y:2018:i:2:p:38-41
    DOI: 10.19080/BBOAJ.2018.06.555682
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    References listed on IDEAS

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