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
碩士 === 國立中興大學 === 應用數學系所 === 96 === 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 simultaneou...
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| Format: | Others |
| Language: | en_US |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/12867208912527892400 |
| Summary: | 碩士 === 國立中興大學 === 應用數學系所 === 96 === 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.
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