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Conference Paper: Transcription factor activity estimation based on particle swarm optimization and fast network component analysis

TitleTranscription factor activity estimation based on particle swarm optimization and fast network component analysis
Authors
KeywordsMedical sciences
Computer applications
Issue Date2010
PublisherIEEE.
Citation
2010 Annual International Conference Of The Ieee Engineering In Medicine And Biology Society, Embc'10, 2010, p. 1061-1064 How to Cite?
AbstractTranscription factors (TFs) play an important role in regulating the expression of genes. The accurate measurement of transcription factor activities (TFAs) depends on a series of experimental technologies of molecular biology and is intractable in most practical situations. Some signal processing methods for blind source separation have been applied in the prediction of TFAs from gene expression data. Most of such methods make use of statistical properties of the gene expression data only, leading to the inaccurate detection of TFAs. In contrast, network component analysis (NCA) can provide much improved result through utilizing the structural information of the gene regulatory network. However, the structure of the gene regulatory network, required by NCA, is not available in most practical cases so that NCA is not directly applicable. In this paper, we propose to use particle swarm optimization (PSO) to find the most plausible network structure iteratively from the gene expression data, with the assistance of recently developed fast algorithm for network component analysis (FastNCA). This novel approach to TFA inference can thus take advantage of NCA, even when the required network structure is unknown. The effectiveness of our novel approach has been demonstrated by applications to both simulated data and real gene expression microarray data, in the sense that TFAs can be inferred with high accuracy. © 2010 IEEE.
DescriptionProceedings of the IEEE Engineering in Medicine and Biology Society Conference, 2010, p. 1061-1064
Persistent Identifierhttp://hdl.handle.net/10722/129700
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChen, Wen_HK
dc.contributor.authorChang, Cen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-12-23T08:41:07Z-
dc.date.available2010-12-23T08:41:07Z-
dc.date.issued2010en_HK
dc.identifier.citation2010 Annual International Conference Of The Ieee Engineering In Medicine And Biology Society, Embc'10, 2010, p. 1061-1064en_HK
dc.identifier.issn1557-170Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/129700-
dc.descriptionProceedings of the IEEE Engineering in Medicine and Biology Society Conference, 2010, p. 1061-1064-
dc.description.abstractTranscription factors (TFs) play an important role in regulating the expression of genes. The accurate measurement of transcription factor activities (TFAs) depends on a series of experimental technologies of molecular biology and is intractable in most practical situations. Some signal processing methods for blind source separation have been applied in the prediction of TFAs from gene expression data. Most of such methods make use of statistical properties of the gene expression data only, leading to the inaccurate detection of TFAs. In contrast, network component analysis (NCA) can provide much improved result through utilizing the structural information of the gene regulatory network. However, the structure of the gene regulatory network, required by NCA, is not available in most practical cases so that NCA is not directly applicable. In this paper, we propose to use particle swarm optimization (PSO) to find the most plausible network structure iteratively from the gene expression data, with the assistance of recently developed fast algorithm for network component analysis (FastNCA). This novel approach to TFA inference can thus take advantage of NCA, even when the required network structure is unknown. The effectiveness of our novel approach has been demonstrated by applications to both simulated data and real gene expression microarray data, in the sense that TFAs can be inferred with high accuracy. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE.-
dc.relation.ispartof2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10en_HK
dc.rightsIEEE Engineering in Medicine and Biology Society Conference Proceedings. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 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.subjectMedical sciences-
dc.subjectComputer applications-
dc.titleTranscription factor activity estimation based on particle swarm optimization and fast network component analysisen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1557-170X&volume=2010&spage=1061&epage=1064&date=2010&atitle=Transcription+factor+activity+estimation+based+on+particle+swarm+optimization+and+fast+network+component+analysis-
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.naturepublished_or_final_version-
dc.identifier.doi10.1109/IEMBS.2010.5627641en_HK
dc.identifier.pmid21096999-
dc.identifier.scopuseid_2-s2.0-78650817798en_HK
dc.identifier.hkuros178053en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78650817798&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2010-
dc.identifier.spage1061en_HK
dc.identifier.epage1064en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChen, W=36012338300en_HK
dc.identifier.scopusauthoridChang, C=7407033052en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK

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