Presentation 2005-01-27
A basic study of genetic network estimation with Bayesian network
Kimi SO, Takeo OKAZAKI,
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Abstract(in English) The Bayesian network is a probability network which builds a network from the dependency of causal relationship and correlation between variables. It consists of DAG and conditional probability. On the other hand, in the field of bioinformatics, an analysis of genetic network is one of the research projects. A gene affects other genes. The conditional probability model which made each gene the variable can express this relation. In this paper, we propose the procedure of genetic network estimation with Bayesian network. It is known that the gene expression data used for analysis of a genetic network has large-scale noise. Then, this procedure can consider other information besides gene expression data as constraint. We also conducted the evaluation experiment for verifying whether suitable graph can be chosen with the procedure. So, the result of this experiment is also reported collectively.
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Keyword(in English) Bayesian network / genetic network / matrix representation / DAG / gene expression data
Paper # CST2004-43
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Committee CST
Conference Date 2005/1/20(1days)
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Registration To Concurrent System Technology (CST)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A basic study of genetic network estimation with Bayesian network
Sub Title (in English)
Keyword(1) Bayesian network
Keyword(2) genetic network
Keyword(3) matrix representation
Keyword(4) DAG
Keyword(5) gene expression data
1st Author's Name Kimi SO
1st Author's Affiliation Graduate School of Science and Engineering, University of the Ryukyus()
2nd Author's Name Takeo OKAZAKI
2nd Author's Affiliation Faculty of Engineering, University of the Ryukyus
Date 2005-01-27
Paper # CST2004-43
Volume (vol) vol.104
Number (no) 593
Page pp.pp.-
#Pages 5
Date of Issue