Inferring method of the gene regulatory networks using neural networks adopting a majority rule

The regulatory interaction between gene expressions is considered as a universal mechanism in biological systems and such a mechanism of interactions has been modeled as gene regulatory networks. The gene regulatory networks show a correlation among gene expressions. A lot of methods to describe the gene regulatory network have been developed. Especially, owing to the technologies such as DNA microarrays that provide a number of time course data of gene expressions, the gene regulatory network models described by differential equations have been proposed and developed in recently. To infer such a gene regulatory network using differential equations, it is necessary to approximate many unknown functions from the time course data of gene expressions that is obtained experimentally. One of the successful inference methods of the gene regulatory networks is the method using the neural network. In this study, to improve a performance of the inference, we propose the inferring method of the gene regulatory networks using neural networks adopting a kind of majority rule. Simulation results show the validity of the proposed method.