Monte Carlo-based Mouse Nuclear Receptor Superfamily Gene Regulatory Network Prediction: Stochastic Dynamical System on Graph with Zipf Prior
-
- Kitamura Yusuke
- Waseda University
-
- Kimiwada Tomomi
- National Center of Neurology and Psychiatry
-
- Maruyama Jun
- Waseda University
-
- Kaburagi Takashi
- Waseda University
-
- Matsumoto Takashi
- Waseda University
-
- Wada Keiji
- National Center of Neurology and Psychiatry
Abstract
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.
Journal
-
- Information and Media Technologies
-
Information and Media Technologies 5 (2), 503-518, 2010
Information and Media Technologies Editorial Board
- Tweet
Details 詳細情報について
-
- CRID
- 1390282680241618688
-
- NII Article ID
- 130000251509
-
- ISSN
- 18810896
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- CiNii Articles
-
- Abstract License Flag
- Disallowed