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

Mojtaba Ganjali and Taban Baghfalaki
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Mojtaba Ganjali: Department of Statistics, Shahid Beheshti University, Iran
Taban Baghfalaki: Department of Statistics, Tarbiat Modares University, Iran

Biostatistics and Biometrics Open Access Journal, 2018, vol. 6, issue 2, 38-41

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

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:6:y:2018:i:2:p:38-41

DOI: 10.19080/BBOAJ.2018.06.555682

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