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

• Item type: Trans(1)
• 公開日: 2010-03-15
• タイトル: Monte Carlo-based Mouse Nuclear Receptor Superfamily Gene Regulatory Network Prediction: Stochastic Dynamical System on Graph with Zipf Prior
• タイトル / 言語: en
• タイトル: Monte Carlo-based Mouse Nuclear Receptor Superfamily Gene Regulatory Network Prediction: Stochastic Dynamical System on Graph with Zipf Prior
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Original Paper
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Waseda University
• 著者所属: National Center of Neurology and Psychiatry
• 著者所属: Waseda University
• 著者所属: Waseda University
• 著者所属: Waseda University
• 著者所属: National Center of Neurology and Psychiatry
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者所属(英): en
• 著者所属(英): National Center of Neurology and Psychiatry
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者所属(英): en
• 著者所属(英): National Center of Neurology and Psychiatry
• 著者名: Yusuke, Kitamura; Tomomi, Kimiwada; Jun, Maruyama; Takashi, Kaburagi; Takashi, Matsumoto; Keiji, Wada
• 著者名(英): Yusuke, Kitamura; Tomomi, Kimiwada; Jun, Maruyama; Takashi, Kaburagi; Takashi, Matsumoto; Keiji, Wada
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12177013
• 書誌情報: IPSJ Transactions on Bioinformatics（TBIO）; 巻 3, p. 24-39, 発行日 2010-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6679
• 出版者 / 言語: ja
• 出版者: 情報処理学会