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Vohradský has proposed a neural network model to describe biochemical networks. Based on this model, several researchers have proposed genetic network inference methods. When trying to analyze large-scale genetic networks, however, these methods must solve high-dimensional function optimization problems. In order to resolve the high-dimensionality in the estimation of the parameters of the Vohradský's...
For the inference of S-system models of genetic networks, this study proposes a new method, i.e., a two-phase estimation method. The two-phase estimation method is an extension of the decoupling approach proposed by Voit and Almeida. The decoupling approach defines the estimation of S-system parameters as a problem of solving sets of non-linear algebraic equations. Our method first transforms each...
Recently, we proposed a genetic network inference method using linear programming machines (LPMs). As this method infers genetic networks by solving linear programming problems, its computational time is very short. However, generic networks inferred by the method using the LPMs often contain a large number of false-positive regulations. When we try to apply the inference method to actual problems,...
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