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Conference Paper: Nonnegative network component analysis by linear programming for gene regulatory network reconstruction
Title | Nonnegative network component analysis by linear programming for gene regulatory network reconstruction |
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Authors | |
Keywords | Conventional methods Gene expression microarray Gene regulatory networks Linear programming problem Microarray data |
Issue Date | 2009 |
Publisher | Springer 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? |
Abstract | We 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. |
Description | LNCS v. 5441 is Proceedings of the 8th International Conference, ICA 2009 |
Persistent Identifier | http://hdl.handle.net/10722/61935 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
References |
DC Field | Value | Language |
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dc.contributor.author | Chang, C | en_HK |
dc.contributor.author | Ding, Z | en_HK |
dc.contributor.author | Hung, YS | en_HK |
dc.date.accessioned | 2010-07-13T03:50:31Z | - |
dc.date.available | 2010-07-13T03:50:31Z | - |
dc.date.issued | 2009 | en_HK |
dc.identifier.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 | en_HK |
dc.identifier.issn | 0302-9743 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/61935 | - |
dc.description | LNCS v. 5441 is Proceedings of the 8th International Conference, ICA 2009 | - |
dc.description.abstract | We 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.language | eng | en_HK |
dc.publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ | en_HK |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_HK |
dc.rights | The original publication is available at www.springerlink.com | - |
dc.subject | Conventional methods | - |
dc.subject | Gene expression microarray | - |
dc.subject | Gene regulatory networks | - |
dc.subject | Linear programming problem | - |
dc.subject | Microarray data | - |
dc.title | Nonnegative network component analysis by linear programming for gene regulatory network reconstruction | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Chang, C: cqchang@eee.hku.hk | en_HK |
dc.identifier.email | Hung, YS: yshung@hkucc.hku.hk | en_HK |
dc.identifier.authority | Chang, C=rp00095 | en_HK |
dc.identifier.authority | Hung, YS=rp00220 | en_HK |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1007/978-3-642-00599-2_50 | en_HK |
dc.identifier.scopus | eid_2-s2.0-67149094741 | en_HK |
dc.identifier.hkuros | 163905 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-67149094741&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 5441 | en_HK |
dc.identifier.spage | 395 | en_HK |
dc.identifier.epage | 402 | en_HK |
dc.publisher.place | Germany | en_HK |
dc.description.other | 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 | - |
dc.identifier.scopusauthorid | Chang, C=7407033052 | en_HK |
dc.identifier.scopusauthorid | Ding, Z=7401550510 | en_HK |
dc.identifier.scopusauthorid | Hung, YS=8091656200 | en_HK |
dc.identifier.issnl | 0302-9743 | - |