Using gene expression programming to infer gene regulatory networks from time-series data

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@Article{journals/candc/ZhangPZSZZ13,
  author =       "Yongqing Zhang and Yi-Fei Pu and Haisen Zhang and 
                 Yabo Su and Lifang Zhang and Jiliu Zhou",
  title =        "Using gene expression programming to infer gene
                 regulatory networks from time-series data",
  journal =      "Computational Biology and Chemistry",
  year =         "2013",
  volume =       "47",
  bibdate =      "2013-12-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/candc/candc47.html#ZhangPZSZZ13",
  pages =        "198--206",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, GEP, Gene regulatory networks,
                 Ordinary differential equation, Least mean square",
  URL =          "http://dx.doi.org/10.1016/j.compbiolchem.2013.09.004",
  DOI =          "doi:10.1016/j.compbiolchem.2013.09.004",
  abstract =     "Gene regulatory networks inference is currently a
                 topic under heavy research in the systems biology
                 field. In this paper, gene regulatory networks are
                 inferred via evolutionary model based on time-series
                 microarray data. A non-linear differential equation
                 model is adopted. Gene expression programming (GEP) is
                 applied to identify the structure of the model and
                 least mean square (LMS) is used to optimize the
                 parameters in ordinary differential equations (ODEs).
                 The proposed work has been first verified by synthetic
                 data with noise-free and noisy time-series data,
                 respectively, and then its effectiveness is confirmed
                 by three real time-series expression datasets. Finally,
                 a gene regulatory network was constructed with 12 Yeast
                 genes. Experimental results demonstrate that our model
                 can improve the prediction accuracy of microarray
                 time-series data effectively.",
}

Genetic Programming entries for Yongqing Zhang Yi-Fei Pu Haisen Zhang Yabo Su Lifang Zhang Jiliu Zhou

Citations