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Conference Paper: On Construction of Sparse Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix

TitleOn Construction of Sparse Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix
Authors
Cui, LBLi, WChing, WK
KeywordsProbabilistic Boolean Networks
Inverse Problem
Transition Probability Matrix
Sparse
Issue Date2010
PublisherWorld Publishing Corporation. The Journal's web site is located at http://www.aporc.org/LNOR/
Citation
4th International Conference on Computational Systems Biology (ISB2010), Suzhou, China, 9-11 Sepetember 2010. In Lecture Notes in Operational Research, 2010, v. 13, p. 227-234 How to Cite?
AbstractProbabilistic Boolean Networks (PBNs) are useful models for modeling genetic regulatory networks. In this paper, we propose efficient algorithms for constructing a sparse probabilistic Boolean network when its transition probability matrix and a set of possible Boolean networks are given. This is an interesting inverse problem in network inference and it is important in the sense that most microarray data sets are assumed to be obtained from sampling the steady-state.
Persistent Identifierhttp://hdl.handle.net/10722/128315
ISBN
9787510024078

 

DC FieldValueLanguage
dc.contributor.authorCui, LBen_HK
dc.contributor.authorLi, Wen_HK
dc.contributor.authorChing, WKen_HK
dc.date.accessioned2010-10-31T14:18:34Z-
dc.date.available2010-10-31T14:18:34Z-
dc.date.issued2010en_HK
dc.identifier.citation4th International Conference on Computational Systems Biology (ISB2010), Suzhou, China, 9-11 Sepetember 2010. In Lecture Notes in Operational Research, 2010, v. 13, p. 227-234-
dc.identifier.isbn9787510024078-
dc.identifier.urihttp://hdl.handle.net/10722/128315-
dc.description.abstractProbabilistic Boolean Networks (PBNs) are useful models for modeling genetic regulatory networks. In this paper, we propose efficient algorithms for constructing a sparse probabilistic Boolean network when its transition probability matrix and a set of possible Boolean networks are given. This is an interesting inverse problem in network inference and it is important in the sense that most microarray data sets are assumed to be obtained from sampling the steady-state.-
dc.languageengen_HK
dc.publisherWorld Publishing Corporation. The Journal's web site is located at http://www.aporc.org/LNOR/-
dc.relation.ispartofLecture Notes in Operational Research-
dc.subjectProbabilistic Boolean Networks-
dc.subjectInverse Problem-
dc.subjectTransition Probability Matrix-
dc.subjectSparse-
dc.titleOn Construction of Sparse Probabilistic Boolean Networks from a Prescribed Transition Probability Matrixen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=9787510024078&volume=13&spage=227&epage=234&date=2010&atitle=On+Construction+of+Sparse+Probabilistic+Boolean+Networks+from+a+Prescribed+Transition+Probability+Matrix-
dc.identifier.emailChing, WK: wching@HKUCC.hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.hkuros179079en_HK
dc.identifier.volume13en_HK
dc.identifier.spage227-
dc.identifier.epage234-

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