Function Approximation Approach to the Inference of Neural Network Models of Genetic Networks

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概要

• 論文の詳細を見る

• A model based on a set of differential equations can effectively capture various dynamics. This type of model is therefore ideal for describing genetic networks. Several genetic network inference algorithms based on models of this type have been proposed. Most of these inference methods use models based on a set of differential equations of the fixed form to describe genetic networks. In this study, we propose a new method for the inference of genetic networks. To describe genetic networks, the proposed method does not use models of the fixed form, but uses neural network models. In order to interpret obtained neural network models, we also propose a method based on sensitivity analysis. The effectiveness of the proposed methods is verified through a series of artificial genetic network inference problems.

• 一般社団法人 情報処理学会の論文

著者

• Hatakeyama Mariko

  RIKEN

• Kimura Shuhei

  Faculty Of Engineering Tottori University

• Yamane Soichiro

  Jfe R&d Corporation

• Sonoda Katsuki

  Jfe R&d Corporation

• Matsumura Koki

  Faculty Of Engineering Tottori University

関連論文

• 3P-168 生細胞内蛋白質拡散と相互作用解析から見るErbBシグナル伝達ネットワーク(細胞生物的課題(接着,運動,骨格,伝達,膜),第47回日本生物物理学会年会)
• Function Approximation Approach to the Inference of Neural Network Models of Genetic Networks
• Function Approximation Approach to the Inference of Neural Network Models of Genetic Networks

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