Development of an efficient parameter estimation method for the inference of Vohradský's neural network models of genetic networks

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 neural network model, this study proposes a new method. The proposed method estimates the parameters of the neural network model by solving two-dimensional function optimization problems. Although these two-dimensional problems are non-linear, their low-dimensionality would make the estimation of the model parameters easier. Finally, we confirm the effectiveness of the proposed method through numerical experiments.