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Conference Paper: Nonnegative network component analysis by linear programming for gene regulatory network reconstruction

TitleNonnegative network component analysis by linear programming for gene regulatory network reconstruction
Authors
KeywordsConventional methods
Gene expression microarray
Gene regulatory networks
Linear programming problem
Microarray data
Issue Date2009
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 8th International Conference on Independent Component Analysis and Signal Separation (ICA 2009), Paraty, Brazil, 15-18 March 2009. In Lecture Notes in Computer Science, 2009, v. 5441, p. 395-402 How to Cite?
AbstractWe consider a systems biology problem of reconstructing gene regulatory network from time-course gene expression microarray data, a special blind source separation problem for which conventional methods cannot be applied. Network component analysis (NCA), which makes use of the structural information of the mixing matrix, is a tailored method for this specific blind source separation problem. In this paper, a new NCA method called nonnegative NCA (nnNCA) is proposed to take into account of the non-negativity constraint on the mixing matrix that is based on a reasonable biological assumption. The nnNCA problem is formulated as a linear programming problem which can be solved effectively. Simulation results on spectroscopy data and experimental results on time-course microarray data of yeast cell cycle demonstrate the effectiveness and anti-noise robustness of the proposed nnNCA method. © Springer-Verlag Berlin Heidelberg 2009.
DescriptionLNCS v. 5441 is Proceedings of the 8th International Conference, ICA 2009
Persistent Identifierhttp://hdl.handle.net/10722/61935
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorChang, Cen_HK
dc.contributor.authorDing, Zen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-07-13T03:50:31Z-
dc.date.available2010-07-13T03:50:31Z-
dc.date.issued2009en_HK
dc.identifier.citationThe 8th International Conference on Independent Component Analysis and Signal Separation (ICA 2009), Paraty, Brazil, 15-18 March 2009. In Lecture Notes in Computer Science, 2009, v. 5441, p. 395-402en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/61935-
dc.descriptionLNCS v. 5441 is Proceedings of the 8th International Conference, ICA 2009-
dc.description.abstractWe consider a systems biology problem of reconstructing gene regulatory network from time-course gene expression microarray data, a special blind source separation problem for which conventional methods cannot be applied. Network component analysis (NCA), which makes use of the structural information of the mixing matrix, is a tailored method for this specific blind source separation problem. In this paper, a new NCA method called nonnegative NCA (nnNCA) is proposed to take into account of the non-negativity constraint on the mixing matrix that is based on a reasonable biological assumption. The nnNCA problem is formulated as a linear programming problem which can be solved effectively. Simulation results on spectroscopy data and experimental results on time-course microarray data of yeast cell cycle demonstrate the effectiveness and anti-noise robustness of the proposed nnNCA method. © Springer-Verlag Berlin Heidelberg 2009.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectConventional methods-
dc.subjectGene expression microarray-
dc.subjectGene regulatory networks-
dc.subjectLinear programming problem-
dc.subjectMicroarray data-
dc.titleNonnegative network component analysis by linear programming for gene regulatory network reconstructionen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChang, C: cqchang@eee.hku.hken_HK
dc.identifier.emailHung, YS: yshung@hkucc.hku.hken_HK
dc.identifier.authorityChang, C=rp00095en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1007/978-3-642-00599-2_50en_HK
dc.identifier.scopuseid_2-s2.0-67149094741en_HK
dc.identifier.hkuros163905en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-67149094741&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5441en_HK
dc.identifier.spage395en_HK
dc.identifier.epage402en_HK
dc.publisher.placeGermanyen_HK
dc.description.otherThe 8th International Conference on Independent Component Analysis and Signal Separation (ICA 2009), Paraty, Brazil, 15-18 March 2009. In Lecture Notes in Computer Science, 2009, v. 5441, p. 395-402-
dc.identifier.scopusauthoridChang, C=7407033052en_HK
dc.identifier.scopusauthoridDing, Z=7401550510en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK

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