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A key issue in genomic signal processing is the inference of gene regulatory networks. These are used both to understand the role of biological regulation in phenotypic determination and to derive therapeutic strategies for genetic-based diseases. In this paper, gene regulatory networks are inferred via evolutionary modeling based on time-series microarray measurements. A nonlinear differential equation...
In this paper, the problem of genetic regulatory network inference from time series microarray experiment data is considered. A noisy sigmoidal model is proposed to include both system noise and measurement noise. In order to solve this nonlinear identification problem (with noise), a joint genetic algorithm and Kalman filtering approach is proposed. Genetic algorithm is applied to minimize the fitness...
In this paper, gene regulatory networks are infered through evolutionary modeling and time-series microarray measurements. A nonlinear differential equation model is adopted and an iterative algorithm is proposed to identify the model, where genetic programming is applied to identify the structure of the model and Kalman filtering is employed to estimate the parameters in each iteration. Simulation...
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