File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A new optimization algorithm for network component analysis based on convex programming

TitleA new optimization algorithm for network component analysis based on convex programming
Authors
KeywordsConvex programming
Gene regulatory networks
Microarray
Network component analysis
Issue Date2009
PublisherI E E E. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000002
Citation
Icassp, Ieee International Conference On Acoustics, Speech And Signal Processing - Proceedings, 2009, p. 509-512 How to Cite?
AbstractNetwork component analysis (NCA) has been established as a promising tool for reconstructing gene regulatory networks from microarray data. NCA is a method that can resolve the problem of blind source separation when the mixing matrix instead has a known sparse structure despite the correlation among the source signals. The original NCA algorithm relies on alternating least squares (ALS) and suffers from local convergence as well as slow convergence. In this paper, we develop new and more robust NCA algorithms by incorporating additional signal constraints. In particular, we introduce the biologically sound constraints that all nonzero entries in the connectivity network are positive. Our new approach formulates a convex optimization problem which can be solved efficiently and effectively by fast convex programming algorithms. We verify the effectiveness and robustness of our new approach using simulations and gene regulatory network reconstruction from experimental yeast cell cycle microarray data. ©2009 IEEE.
DescriptionProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, p. 509-512
Paper no. 2203
Persistent Identifierhttp://hdl.handle.net/10722/62038
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChang, Cen_HK
dc.contributor.authorYeung, SHen_HK
dc.contributor.authorDing, Zen_HK
dc.date.accessioned2010-07-13T03:52:37Z-
dc.date.available2010-07-13T03:52:37Z-
dc.date.issued2009en_HK
dc.identifier.citationIcassp, Ieee International Conference On Acoustics, Speech And Signal Processing - Proceedings, 2009, p. 509-512en_HK
dc.identifier.issn1520-6149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/62038-
dc.descriptionProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, p. 509-512en_HK
dc.descriptionPaper no. 2203-
dc.description.abstractNetwork component analysis (NCA) has been established as a promising tool for reconstructing gene regulatory networks from microarray data. NCA is a method that can resolve the problem of blind source separation when the mixing matrix instead has a known sparse structure despite the correlation among the source signals. The original NCA algorithm relies on alternating least squares (ALS) and suffers from local convergence as well as slow convergence. In this paper, we develop new and more robust NCA algorithms by incorporating additional signal constraints. In particular, we introduce the biologically sound constraints that all nonzero entries in the connectivity network are positive. Our new approach formulates a convex optimization problem which can be solved efficiently and effectively by fast convex programming algorithms. We verify the effectiveness and robustness of our new approach using simulations and gene regulatory network reconstruction from experimental yeast cell cycle microarray data. ©2009 IEEE.en_HK
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000002en_HK
dc.relation.ispartofICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedingsen_HK
dc.rightsIEEE International Conference on Acoustics, Speech and Signal Processing Proceedings. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectConvex programmingen_HK
dc.subjectGene regulatory networksen_HK
dc.subjectMicroarrayen_HK
dc.subjectNetwork component analysisen_HK
dc.titleA new optimization algorithm for network component analysis based on convex programmingen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChang, C: cqchang@eee.hku.hken_HK
dc.identifier.authorityChang, C=rp00095en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.scopuseid_2-s2.0-70349451744en_HK
dc.identifier.hkuros163904en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70349451744&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage509en_HK
dc.identifier.epage512en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChang, C=7407033052en_HK
dc.identifier.scopusauthoridYeung, SH=35090579800en_HK
dc.identifier.scopusauthoridDing, Z=7401550510en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats