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Conference Paper: Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle

TitleModelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle
Authors
KeywordsTranscription regulation
Network component analysis
Microarray
Issue Date2009
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000002
Citation
The 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), Taipei, Taiwan, 19-24 April 2009. In IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, 2009, p. 1769-1772 How to Cite?
AbstractUsing high throughput DNA binding data for transcription factors and DNA microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (NCA) approach. We incorporated probe level uncertainties in gene expression measurements into the NCA analysis by the application of probabilistic principal component analysis (PPCA), and applied the method to data from yeast metabolic cycle. Analysis shows statistically significant enhancement to periodicity in a large fraction of the transcription factor activities inferred from the model. For several of these we found literature evidence of post-transcriptional regulation. Accounting for probe level uncertainty of microarray measurements leads to improved network component analysis. Transcription factor profiles showing greater periodicity at their activity levels, rather than at the corresponding mRNA levels, for over half the regulators in the networks points to extensive post-transcriptional regulations. ©2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/62089
ISBN
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChang, CQen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorNiranjan, Men_HK
dc.date.accessioned2010-07-13T03:53:40Z-
dc.date.available2010-07-13T03:53:40Z-
dc.date.issued2009en_HK
dc.identifier.citationThe 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), Taipei, Taiwan, 19-24 April 2009. In IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, 2009, p. 1769-1772en_HK
dc.identifier.isbn978-1-4244-2354-5en_HK
dc.identifier.issn1520-6149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/62089-
dc.description.abstractUsing high throughput DNA binding data for transcription factors and DNA microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (NCA) approach. We incorporated probe level uncertainties in gene expression measurements into the NCA analysis by the application of probabilistic principal component analysis (PPCA), and applied the method to data from yeast metabolic cycle. Analysis shows statistically significant enhancement to periodicity in a large fraction of the transcription factor activities inferred from the model. For several of these we found literature evidence of post-transcriptional regulation. Accounting for probe level uncertainty of microarray measurements leads to improved network component analysis. Transcription factor profiles showing greater periodicity at their activity levels, rather than at the corresponding mRNA levels, for over half the regulators in the networks points to extensive post-transcriptional regulations. ©2009 IEEE.en_HK
dc.languageengen_HK
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000002en_HK
dc.relation.ispartofIEEE International Conference on Acoustics, Speech and Signal Processing Proceedingsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_HK
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.subjectTranscription regulationen_HK
dc.subjectNetwork component analysisen_HK
dc.subjectMicroarrayen_HK
dc.titleModelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycleen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-2353-8 &volume=1- 8&spage=1769&epage=1772&date=2009&atitle=MODELLING+UNCERTAINTY+IN+TRANSCRIPTOME+MEASUREMENTS+ENHANCES+NETWORK+COMPONENT+ANALYSIS+OF+YEAST+METABOLIC+CYCLEen_HK
dc.identifier.emailChang, CQ: cqchang@eee.hku.hken_HK
dc.identifier.emailHung, YS: yshung@hkucc.hku.hken_HK
dc.identifier.authorityChang, CQ=rp00095en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICASSP.2009.4959947en_HK
dc.identifier.scopuseid_2-s2.0-70349205527en_HK
dc.identifier.hkuros165278en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70349205527&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.isiWOS:000268919200443-
dc.publisher.placeUnited Statesen_HK

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