NDLI: Learning multi-time delay gene network using Bayesian network framework

Content Provider	IEEE Xplore Digital Library
Author	Tie-Fei Liu Wing-Kin Sung Mittal, A.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore (Tie-Fei Liu; Wing-Kin Sung)
Abstract	Exact determination of gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Most research work in learning gene networks either assumes that there is no time delay in gene expression or that there is a constant time delay. The paper shows how Bayesian networks can be applied to represent multitime delay relationships as well as directed loops. The intractability of the network learning algorithm is handled by using an improved mutual information criteria. Also, a new structure learning algorithm, "learning by modification", is proposed to learn the sparse structure of a gene network. The experimental results on synthetic data and real data show that our method is more accurate in determining the gene structure as compared to the traditional methods. Even for transcriptional loops spanning over the whole cell, our algorithm can detect them.
Sponsorship	IEEE Comput. Soc.
Starting Page	640
Ending Page	645
File Size	214456
Page Count	6
File Format	PDF
ISBN	076952236X
ISSN	10823409
DOI	10.1109/ICTAI.2004.79
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Delay effects Gene expression Biological system modeling Mutual information Computer science Computer networks Organisms Data mining Time series analysis learning by modification Gene Network Bayesian Networks mutual information
Content Type	Text
Resource Type	Article

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