NDLI: HMM training using correlation coefficients of time-series gene expression data

Content Provider	IEEE Xplore Digital Library
Author	Jiangeng Li Qinglei Guo Yiheng He
Copyright Year	2012
Description	Author affiliation: Institute of Artificial Intelligence and Robots, College of Electronic Information and Control Engineering, Beijing University of Technology, 100124, China (Jiangeng Li; Qinglei Guo; Yiheng He)
Abstract	In the processes of gene expressing, gene expression data at each time point is different. Each gene expression levels in a time point affects gene expression levels in next time point. It is a hot point to construct gene regular network using time series gene expression data. There are many methods being used for this work, such as Boolean networks, Differential equations, Bayesian networks and so on. In this paper, we build transfer relationship of gene as gene observation matrix by correlation coefficients and P_value of time-series gene expression data in adjacent time points. And then we get gene states transfer probability by training HMM using gene observation matrix, and build gene regular network corresponding it. By comparing with real network, our experiment provides good result, and the method has less computation complexity than other regular methods like dynamic Bayesian networks.
Starting Page	3719
Ending Page	3723
File Size	608823
Page Count	5
File Format	PDF
ISBN	9781457720734
e-ISBN	9781457720741
DOI	10.1109/CCDC.2012.6244596
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training gene expression data Correlation Bayesian methods Computational modeling Time series analysis Hidden Markov models HMM time series data Gene expression gene regular network correlation coefficient
Content Type	Text
Resource Type	Article

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