The Parameter Optimization in the Inference of Gene Regulatory Network by Neural Networks Adopting Majority Rule

Yasuki, Hirai; Kizaki, Naoyuki; Yoshino, Hiroshi; Kurokawa, Haruhisa

doi:10.15248/proc.1.648

Cited by 1 publication

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“…The differential equation models describe the dynamical characteristics of gene expressions based on the information of correlations among genes. The differential equation models are considered as one of the most outstanding tools to describe a mechanism of mutual interactions of genes, metabolism and so on [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

The inference method of the gene regulatory network with a majority rule

Kizaki

Yoshino

Kurokawa

2015

NOLTA

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Abstract:The regulatory interactions among genes are summarized by the gene regulatory network. Recently, the gene regulatory network that is described by the differential equations is widely used, and a lot of inference methods using time course data of the gene expression levels have been proposed. One of the successful inference methods of the gene regulatory network is the method using the neural network. In this study, as a method to improve a performance of the gene regulatory network inference using the neural networks, we propose the method to apply a kind of majority rule to the conventional method. Our proposed method infers the regulatory interactions in the gene network based on the results of a lot of trials of the inference using neural networks. In the simulations, we evaluate our proposed method using artificially defined gene regulatory networks. The results show the validity of the proposed method. The results also suggest that the strategy of the proposed method is applicable to various methods using the heuristic solver.

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