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Conference Paper: Application of Granger causality to gene regulatory network discovery

TitleApplication of Granger causality to gene regulatory network discovery
Authors
KeywordsDNA microarray
Gene regulatory network
Granger causality
Model validation
Pairwise
Spurious discovery
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515
Citation
The 6th IEEE International Conference on Systems Biology (ISB 2012), Xi'an, China, 18-20 August 2012. In Conference Proceedings, 2012, p. 232-239 How to Cite?
AbstractGranger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions. © 2012 IEEE.
DescriptionArticle no. 6314142
Persistent Identifierhttp://hdl.handle.net/10722/186739
ISBN

 

DC FieldValueLanguage
dc.contributor.authorTam, GHFen_US
dc.contributor.authorChang, Cen_US
dc.contributor.authorHung, YSen_US
dc.date.accessioned2013-08-20T12:19:20Z-
dc.date.available2013-08-20T12:19:20Z-
dc.date.issued2012en_US
dc.identifier.citationThe 6th IEEE International Conference on Systems Biology (ISB 2012), Xi'an, China, 18-20 August 2012. In Conference Proceedings, 2012, p. 232-239en_US
dc.identifier.isbn978-1-4673-4398-5-
dc.identifier.urihttp://hdl.handle.net/10722/186739-
dc.descriptionArticle no. 6314142-
dc.description.abstractGranger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515-
dc.relation.ispartofIEEE International Conference on Systems Biology Proceedingsen_US
dc.rightsIEEE International Conference on Systems Biology Proceedings. Copyright © IEEE.-
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.subjectDNA microarray-
dc.subjectGene regulatory network-
dc.subjectGranger causality-
dc.subjectModel validation-
dc.subjectPairwise-
dc.subjectSpurious discovery-
dc.titleApplication of Granger causality to gene regulatory network discoveryen_US
dc.typeConference_Paperen_US
dc.identifier.emailTam, GHF: hftam@eee.hku.hken_US
dc.identifier.emailChang, C: cqchang@eee.hku.hken_US
dc.identifier.emailHung, YS: yshung@eee.hku.hk-
dc.identifier.authorityChang, C=rp00095en_US
dc.identifier.authorityHung, YS=rp00220en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISB.2012.6314142-
dc.identifier.scopuseid_2-s2.0-84868641202-
dc.identifier.hkuros220041en_US
dc.identifier.spage232-
dc.identifier.epage239-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140103-

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