Monte Carlo-based Mouse Nuclear Receptor Superfamily Gene Regulatory Network Prediction: Stochastic Dynamical System on Graph with Zipf Prior

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概要

• 論文の詳細を見る

• A Monte Carlo based algorithm is proposed to predict gene regulatory network structure of mouse nuclear receptor superfamily, about which little is known although those genes are believed to be related with several difficult diseases. The gene expression data is regarded as sample vector trajectories from a stochastic dynamical system on a graph. The problem is formulated within a Bayesian framework where the graph prior distribution is assumed to follow a Zipf distribution. Appropriateness of a graph is evaluated by the graph posterior mean. The algorithm is implemented with the Exchange Monte Carlo method. After validation against synthesized data, an attempt is made to use the algorithm for predicting network structure of the target, the mouse nuclear receptor superfamily. Several remarks are made on the feasibility of the predicted network from a biological viewpoint.

• 一般社団法人情報処理学会の論文
• 2010-03-15

著者

• Keiji Wada

  National Center Of Neurology And Psychiatry

• Yusuke Kitamura

  Waseda University

• Tomomi Kimiwada

  National Center of Neurology and Psychiatry

• Jun Maruyama

  Waseda University

• Takashi Kaburagi

  Waseda University

• Takashi Matsumoto

  Waseda University

関連論文

• Monte Carlo-based Mouse Nuclear Receptor Superfamily Gene Regulatory Network Prediction: Stochastic Dynamical System on Graph with Zipf Prior

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