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	Dynamic Bayesian network approach for modeling gene regulatory networks
	Zhang Yu ; Deng Zhidong ; Sun Xin ; Jia Peifa
	2010-10-12 ; 2010-10-12
关键词	Practical Theoretical or Mathematical/ belief networks biology computing cellular biophysics expectation-maximisation algorithm genetics particle filtering (numerical methods) sensor fusion/ dynamic Bayesian network gene regulatory network multiple data fusion structural expectation maximization particle filtering network structure biological system gene expression data Saccharomyces Cerevisiae cell cycle data/ A8715 Molecular biophysics A8725 Cellular biophysics A0250 Probability theory, stochastic processes, and statistics C7330 Biology and medical computing C6170K Knowledge engineering techniques C5260A Sensor fusion C1140Z Other topics in statistics
中文摘要	A dynamic Bayesian network-based multiple data fusion method was used to improve the modelling accuracy and the inferred gene regulatory networks. Structural expectation maximization and particle filtering are used to learn the unknown network structure and the parameters in a method that can effectively handle missing and noisy data. The method captures the dynamic nature of the biological system and naturally incorporates other data from transcription factor binding location data into the original gene expression data. The effectiveness of the method is shown by tests on Saccharomyces Cerevisiae cell cycle data. The results show that the sensitivity and specificity of the method are increased by 19% and 95 % for the gene expression data itself and the prediction accuracy is raised further with multiple data sources.
语种	中文
出版者	Tsinghua University Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/82858]
专题	清华大学
推荐引用方式 GB/T 7714	Zhang Yu,Deng Zhidong,Sun Xin,et al. Dynamic Bayesian network approach for modeling gene regulatory networks[J],2010, 2010.
APA	Zhang Yu,Deng Zhidong,Sun Xin,&Jia Peifa.(2010).Dynamic Bayesian network approach for modeling gene regulatory networks..
MLA	Zhang Yu,et al."Dynamic Bayesian network approach for modeling gene regulatory networks".(2010).

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