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Article: On construction of stochastic genetic networks based on gene expression sequences

TitleOn construction of stochastic genetic networks based on gene expression sequences
Authors
Issue Date2005
PublisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijns/ijns.shtml
Citation
International Journal Of Neural Systems, 2005, v. 15 n. 4, p. 297-310 How to Cite?
AbstractReconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model. © World Scientific Publishing Company.
Persistent Identifierhttp://hdl.handle.net/10722/75463
ISSN
2015 Impact Factor: 6.085
2015 SCImago Journal Rankings: 0.909
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorNg, MMen_HK
dc.contributor.authorFung, ESen_HK
dc.contributor.authorAkutsu, Ten_HK
dc.date.accessioned2010-09-06T07:11:21Z-
dc.date.available2010-09-06T07:11:21Z-
dc.date.issued2005en_HK
dc.identifier.citationInternational Journal Of Neural Systems, 2005, v. 15 n. 4, p. 297-310en_HK
dc.identifier.issn0129-0657en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75463-
dc.description.abstractReconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model. © World Scientific Publishing Company.en_HK
dc.languageengen_HK
dc.publisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijns/ijns.shtmlen_HK
dc.relation.ispartofInternational Journal of Neural Systemsen_HK
dc.subject.meshAnimalsen_HK
dc.subject.meshComputer Simulationen_HK
dc.subject.meshGene Expressionen_HK
dc.subject.meshGene Expression Profiling - methodsen_HK
dc.subject.meshGene Expression Regulationen_HK
dc.subject.meshHumansen_HK
dc.subject.meshModels, Geneticen_HK
dc.subject.meshSensitivity and Specificityen_HK
dc.subject.meshStochastic Processesen_HK
dc.subject.meshYeasts - geneticsen_HK
dc.titleOn construction of stochastic genetic networks based on gene expression sequencesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0129-0657&volume=15&spage=297&epage=310&date=2005&atitle=On+Construction+of+Stochastic+Genetic+Networks+Based+on+Gene+Expression+Sequencesen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1142/S0129065705000256en_HK
dc.identifier.pmid16187405-
dc.identifier.scopuseid_2-s2.0-33644618250en_HK
dc.identifier.hkuros103861en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33644618250&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume15en_HK
dc.identifier.issue4en_HK
dc.identifier.spage297en_HK
dc.identifier.epage310en_HK
dc.identifier.isiWOS:000233460200007-
dc.publisher.placeSingaporeen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridNg, MM=34571761900en_HK
dc.identifier.scopusauthoridFung, ES=7005440799en_HK
dc.identifier.scopusauthoridAkutsu, T=7102080520en_HK

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