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Conference Paper: Generating probabilistic Boolean networks from a prescribed transition probability matrix

TitleGenerating probabilistic Boolean networks from a prescribed transition probability matrix
Authors
Issue Date2009
PublisherThe Institution of Engineering and Technology. The Journal's web site is located at http://www.ietdl.org/IP-SYB
Citation
2nd International Symposium on Optimization and Systems Biology (OSB 2008), Lijiang, China, 31 October - 3 November 2008. In IET Systems Biology, 2009, v. 3 n. 6, p. 453-464 How to Cite?
AbstractProbabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state. © 2009 © The Institution of Engineering and Technology.
Persistent Identifierhttp://hdl.handle.net/10722/75388
ISSN
2015 Impact Factor: 0.764
2015 SCImago Journal Rankings: 0.399
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorChen, Xen_HK
dc.contributor.authorTsing, NKen_HK
dc.date.accessioned2010-09-06T07:10:39Z-
dc.date.available2010-09-06T07:10:39Z-
dc.date.issued2009en_HK
dc.identifier.citation2nd International Symposium on Optimization and Systems Biology (OSB 2008), Lijiang, China, 31 October - 3 November 2008. In IET Systems Biology, 2009, v. 3 n. 6, p. 453-464en_HK
dc.identifier.issn1751-8849en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75388-
dc.description.abstractProbabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state. © 2009 © The Institution of Engineering and Technology.en_HK
dc.languageengen_HK
dc.publisherThe Institution of Engineering and Technology. The Journal's web site is located at http://www.ietdl.org/IP-SYBen_HK
dc.relation.ispartofIET Systems Biologyen_HK
dc.subject.meshGene Regulatory Networks-
dc.subject.meshMarkov Chains-
dc.subject.meshModels, Genetic-
dc.subject.meshModels, Statistical-
dc.subject.meshSystems Biology - methods-
dc.titleGenerating probabilistic Boolean networks from a prescribed transition probability matrixen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.emailTsing, NK:nktsing@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.identifier.authorityTsing, NK=rp00794en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1049/iet-syb.2008.0173en_HK
dc.identifier.pmid19947771-
dc.identifier.scopuseid_2-s2.0-71949091165en_HK
dc.identifier.hkuros168367en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-71949091165&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume3en_HK
dc.identifier.issue6en_HK
dc.identifier.spage453en_HK
dc.identifier.epage464en_HK
dc.identifier.eissn1751-8857-
dc.identifier.isiWOS:000272502400003-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridChen, X=24334384300en_HK
dc.identifier.scopusauthoridTsing, NK=6602663351en_HK

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