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標題: 在小型基因調控網路使用時間序列基因表現資料辨識目標基因調控子:數學模型和電腦模擬
The identification of regulators of a target gene in small scale genetic regulatory network using time series gene expression data:Mathematical modeling and computer simultaneous
作者: 楊美芳
Yang, Mei-Fan
關鍵字: genetic regulatory network;基因調控網路;simultaneous recurrent neural network;target gene regulator;time series gene expression data;同時回歸類神經網路;目標基因調控子;時間序列基因表現資料
出版社: 應用數學系所
引用: 1. Bansal, M., G.D. Gatta, and D. di Bernardo, Inference of gene regulatory networks and compound mode of action from time course gene exxpression profiles. Bioinformatics, 2006. 22(7): p. 815-22. 2. Mendes, P., W. Sha, and K. Ye, Artifical gene networks for objective comparison of analysis algorithms. Bioinformaatics, 2003. 19 Suppl 2: p. ii122-9. 3. Nam, D., S.H. Yoon, and J.F. Kim, Ensemble learning of genetic networks from time-series expression data. Bioinformatics, 2007. 23(23): p. 3225-31. 4. Vohradsky, J., Neural model of the genetic network. J Biol Chem, 2001. 276(39): p. 36168-73. 5. Zou, M. and S.D. Conzen, A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 2005. 21(1): p. 71-9. 6. de Jong, H., Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol, 2002. 9(1): p. 67-103. 7. Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA, 1982. 79(8): p. 2554-8. 8. Bose, N.K. and P. Liang, Neural network fundamentals with graphs, algorithms, and applications. Series In Electrical And Computer Engineering 1996, Hightstown, NJ: Mcgraw-Hill. 9. Vu, T.T. and J. Vohradsky, Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae. Nucleic Acids Res, 2007. 35(1): p. 279-87. 10. Vohradsky, J., Neural network model of gene expression. Faseb J, 2001. 15(3): p. 846-54. 11. Vohradsky, J. and C.J. Thompson, Systems level analysis of protein synthesis patterns associated with bacterial growth and metabolic transitions. Proteomics, 2006. 6(3): p. 785-93. 12. Hornik, K., M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators. Neural Networks, 1989. 2(5): p. 359-366. 13. Kohavi, R. and G.H. John, Wrappers for feature subset selection Artificial Intelligence 1997. 97(1-2): p. 273-324.

Gene expression translates information encoded in DNA sequences to produce RNA or proteins. Genes in a living cell interacting with each other through expression products gives rise to the genetic regulatory network (GRN). We present a one hidden layer simultaneous recurrent neural network (SRNN) model formally resembling the principals of GRN. Using time series gene expression data, we apply the model and model selection algorithms to infer regulators of target genes. To make comparisons, we also predict regulators of target genes based on linear and Hopfield neural network models. To objectively evaluate the regulator identification procedure, the time series gene expression data are generated by an independent artificial GRN with well-defined regulatory and kinetic properties.
其他識別: U0005-1706200810361300
Appears in Collections:應用數學系所

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