A computational approach to the functional clustering of periodic gene-expression profiles

Bong-Rae Kim Li Zhang Arthur Berg Jianqing Fan Rongling Wu

Data Analysis mathscidoc:1912.43344

Genetics, 180, (2), 821-834, 2008.10
DNA microarray analysis has emerged as a leading technology to enhance our understanding of gene regulation and function in cellular mechanism controls on a genomic scale. This technology has advanced to unravel the genetic machinery of biological rhythms by collecting massive gene-expression data in a time course. Here, we present a statistical model for clustering periodic patterns of gene expression in terms of different transcriptional profiles. The model incorporates biologically meaningful Fourier series approximations of gene periodic expression into a mixture-model-based likelihood function, thus producing results that are likely to be closer to biological relevance, as compared to those from existing models. Also because the structures of the time-dependent means and covariance matrix are modeled, the new approach displays increased statistical power and precision of parameter estimation. The
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@inproceedings{bong-rae2008a,
  title={A computational approach to the functional clustering of periodic gene-expression profiles},
  author={Bong-Rae Kim, Li Zhang, Arthur Berg, Jianqing Fan, and Rongling Wu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113834924074904},
  booktitle={Genetics},
  volume={180},
  number={2},
  pages={821-834},
  year={2008},
}
Bong-Rae Kim, Li Zhang, Arthur Berg, Jianqing Fan, and Rongling Wu. A computational approach to the functional clustering of periodic gene-expression profiles. 2008. Vol. 180. In Genetics. pp.821-834. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113834924074904.
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