File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Synthetic time series resembling human (HeLa) cell-cycle gene expression data and application to gene regulatory network discovery

TitleSynthetic time series resembling human (HeLa) cell-cycle gene expression data and application to gene regulatory network discovery
Authors
KeywordsGene regulatory network
Synthetic data
Cellcycle
Time series
Vector autoregressive mode
Pearson correlation coefficient
Granger causality
Issue Date2013
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002959
Citation
The 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2013), Hangzhou, Zhejiang, China, 26-27 August 2013. In Conference Proceedings, 2013, v. 2, p. 538-541 How to Cite?
AbstractEvaluation of gene regulatory network (GRN) discovery methods relies heavily on synthetic time series. However, synthetic data generated by traditional method deviate a lot from real data, making such evaluation questionable. Guiding by decaying sinusoids, we propose a new method that generates synthetic data resembling human (HeLa) cell-cycle gene expression data. Using the new synthetic data, a simple comparison between four GRN discovery methods reveals that Granger causality (GC) methods substantially outperform Pearson correlation coefficient (PCC), while time-shifted PCC can give comparable performance as GC methods. The new synthetic data generation would also be useful for generating other kinds of cell-cycle time series. Using data generated by our proposed method, evaluation of GRN discovery methods should be more trustworthy for real-data applications.
Persistent Identifierhttp://hdl.handle.net/10722/186742
ISBN

 

DC FieldValueLanguage
dc.contributor.authorTam, GHFen_US
dc.contributor.authorHung, YSen_US
dc.contributor.authorChang, Cen_US
dc.date.accessioned2013-08-20T12:19:21Z-
dc.date.available2013-08-20T12:19:21Z-
dc.date.issued2013en_US
dc.identifier.citationThe 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2013), Hangzhou, Zhejiang, China, 26-27 August 2013. In Conference Proceedings, 2013, v. 2, p. 538-541en_US
dc.identifier.isbn978-0-7695-5011-4-
dc.identifier.urihttp://hdl.handle.net/10722/186742-
dc.description.abstractEvaluation of gene regulatory network (GRN) discovery methods relies heavily on synthetic time series. However, synthetic data generated by traditional method deviate a lot from real data, making such evaluation questionable. Guiding by decaying sinusoids, we propose a new method that generates synthetic data resembling human (HeLa) cell-cycle gene expression data. Using the new synthetic data, a simple comparison between four GRN discovery methods reveals that Granger causality (GC) methods substantially outperform Pearson correlation coefficient (PCC), while time-shifted PCC can give comparable performance as GC methods. The new synthetic data generation would also be useful for generating other kinds of cell-cycle time series. Using data generated by our proposed method, evaluation of GRN discovery methods should be more trustworthy for real-data applications.-
dc.languageengen_US
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002959-
dc.relation.ispartofInternational Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) Proceedingsen_US
dc.rightsInternational Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2013 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectGene regulatory network-
dc.subjectSynthetic data-
dc.subjectCellcycle-
dc.subjectTime series-
dc.subjectVector autoregressive mode-
dc.subjectPearson correlation coefficient-
dc.subjectGranger causality-
dc.titleSynthetic time series resembling human (HeLa) cell-cycle gene expression data and application to gene regulatory network discoveryen_US
dc.typeConference_Paperen_US
dc.identifier.emailTam, GHF: hftam@eee.hku.hken_US
dc.identifier.emailHung, YS: yshung@eee.hku.hken_US
dc.identifier.emailChang, C: cqchang@eee.hku.hk-
dc.identifier.authorityHung, YS=rp00220en_US
dc.identifier.authorityChang, C=rp00095en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/IHMSC.2013.276-
dc.identifier.hkuros220050en_US
dc.identifier.volume2-
dc.identifier.spage538-
dc.identifier.epage541-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140102-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats