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Conference Paper: Modeling and identification of gene regulatory networks: A Granger causality approach

TitleModeling and identification of gene regulatory networks: A Granger causality approach
Authors
KeywordsGene regulatory network
Granger causality
Regularization
Time-series genomic data
Variable selection
Vector autoregressive model
Issue Date2010
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000424
Citation
The 9th International Conference on Machine Learning and Cybernetics (ICMLC 2010), Qingdao, China, 11-14 July 2010. In Proceedings of the 9th ICMLC, 2010, v. 6, p. 3073-3078 How to Cite?
AbstractIt is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from time-series genomic data, i.e., to explore the interactions among a large number of genes and gene products over time. Currently, one common approach is based on Granger causality, which models the time-series genomic data as a vector autoregressive (VAR) process and estimates the GRNs from the VAR coefficient matrix. The main challenge for identification of VAR models is the high dimensionality of genes and limited number of time points, which results in statistically inefficient solution and high computational complexity. Therefore, fast and efficient variable selection techniques are highly desirable. In this paper, an introductory review of identification methods and variable selection techniques for VAR models in learning the GRNs will be presented. Furthermore, a dynamic VAR (DVAR) model, which accounts for dynamic GRNs changing with time during the experimental cycle, and its identification methods are introduced. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/143347
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorXu, WCen_HK
dc.contributor.authorHu, Yen_HK
dc.date.accessioned2011-11-22T08:31:02Z-
dc.date.available2011-11-22T08:31:02Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 9th International Conference on Machine Learning and Cybernetics (ICMLC 2010), Qingdao, China, 11-14 July 2010. In Proceedings of the 9th ICMLC, 2010, v. 6, p. 3073-3078en_HK
dc.identifier.isbn978-1-4244-6527-9-
dc.identifier.urihttp://hdl.handle.net/10722/143347-
dc.description.abstractIt is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from time-series genomic data, i.e., to explore the interactions among a large number of genes and gene products over time. Currently, one common approach is based on Granger causality, which models the time-series genomic data as a vector autoregressive (VAR) process and estimates the GRNs from the VAR coefficient matrix. The main challenge for identification of VAR models is the high dimensionality of genes and limited number of time points, which results in statistically inefficient solution and high computational complexity. Therefore, fast and efficient variable selection techniques are highly desirable. In this paper, an introductory review of identification methods and variable selection techniques for VAR models in learning the GRNs will be presented. Furthermore, a dynamic VAR (DVAR) model, which accounts for dynamic GRNs changing with time during the experimental cycle, and its identification methods are introduced. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000424-
dc.relation.ispartof2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010en_HK
dc.rightsProceedings of the International Conference on Machine Learning and Cybernetics. Copyright © IEEE.-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectGene regulatory networken_HK
dc.subjectGranger causalityen_HK
dc.subjectRegularizationen_HK
dc.subjectTime-series genomic dataen_HK
dc.subjectVariable selectionen_HK
dc.subjectVector autoregressive modelen_HK
dc.titleModeling and identification of gene regulatory networks: A Granger causality approachen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailZhang, ZG: zhangzg@hku.hken_HK
dc.identifier.emailHung, YS: yshung@hkucc.hku.hken_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.emailXu, WC: wcxu@eee.hku.hken_HK
dc.identifier.emailHu, Y: yhud@hku.hken_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityXu, WC=rp00198en_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1109/ICMLC.2010.5580719en_HK
dc.identifier.scopuseid_2-s2.0-78149347760en_HK
dc.identifier.hkuros174339-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78149347760&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6en_HK
dc.identifier.spage3073en_HK
dc.identifier.epage3078en_HK
dc.publisher.placeUnited States-
dc.description.otherThe 9th International Conference on Machine Learning and Cybernetics (ICMLC 2010), Qingdao, China, 11-14 July 2010. In Proceedings of the 9th ICMLC, 2010, v. 6, p. 3073-3078-
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridXu, WC=7404428876en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK

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