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Article: Building genetic networks for gene expression patterns

TitleBuilding genetic networks for gene expression patterns
Authors
Issue Date2004
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2004, v. 3177, p. 17-24 How to Cite?
AbstractBuilding genetic regulatory networks from time series data of gene expression patterns is an important topic in bioinformatics. Probabilistic Boolean networks (PBNs) have been developed as a model of gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and uncover the relative sensitivity of genes in their interactions with other genes. However, PBNs are unlikely used in practice because of huge number of possible predictors and their computed probabilities. In this paper, we propose a multivariate Markov chain model to govern the dynamics of a genetic network for gene expression patterns. The model preserves the strength of PBNs and reduce the complexity of the networks. Parameters of the model are quadratic with respect to the number of genes. We also develop an efficient estimation method for the model parameters. Simulation results on yeast data are given to illustrate the effectiveness of the model. © Springer-Verlag Berlin Heidelberg 2004.
Persistent Identifierhttp://hdl.handle.net/10722/156166
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_US
dc.contributor.authorFung, ESen_US
dc.contributor.authorNg, MKen_US
dc.date.accessioned2012-08-08T08:40:40Z-
dc.date.available2012-08-08T08:40:40Z-
dc.date.issued2004en_US
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2004, v. 3177, p. 17-24en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/156166-
dc.description.abstractBuilding genetic regulatory networks from time series data of gene expression patterns is an important topic in bioinformatics. Probabilistic Boolean networks (PBNs) have been developed as a model of gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and uncover the relative sensitivity of genes in their interactions with other genes. However, PBNs are unlikely used in practice because of huge number of possible predictors and their computed probabilities. In this paper, we propose a multivariate Markov chain model to govern the dynamics of a genetic network for gene expression patterns. The model preserves the strength of PBNs and reduce the complexity of the networks. Parameters of the model are quadratic with respect to the number of genes. We also develop an efficient estimation method for the model parameters. Simulation results on yeast data are given to illustrate the effectiveness of the model. © Springer-Verlag Berlin Heidelberg 2004.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.titleBuilding genetic networks for gene expression patternsen_US
dc.typeArticleen_US
dc.identifier.emailChing, WK:wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/978-3-540-28651-6_3-
dc.identifier.scopuseid_2-s2.0-33746258589en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33746258589&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume3177en_US
dc.identifier.spage17en_US
dc.identifier.epage24en_US
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridChing, WK=13310265500en_US
dc.identifier.scopusauthoridFung, ES=7005440799en_US
dc.identifier.scopusauthoridNg, MK=34571761900en_US

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