File Download
Supplementary

Conference Paper: Meta-analysis on gene regulatory networks discovered by pairwise Granger causality

TitleMeta-analysis on gene regulatory networks discovered by pairwise Granger causality
Authors
KeywordsGene regulatory network
Meta-analysis
Multiple experiments
Pairwise Granger causality
Fisher's chi-square test
Issue Date2013
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515
Citation
The 7th IEEE International Conference on Systems Biology (ISB 2013), Huangshan, China, 23-25 August 2013. In Conference Proceedings, 2013, p. 123-128 How to Cite?
AbstractIdentifying regulatory genes partaking in disease development is important to medical advances. Since gene expression data of multiple experiments exist, combining results from multiple gene regulatory network discoveries offers higher sensitivity and specificity. However, data for multiple experiments on the same problem may not possess the same set of genes, and hence many existing combining methods are not applicable. In this paper, we approach this problem using a number of meta-analysis methods and compare their performances. Simulation results show that vote counting is outperformed by methods belonging to the Fisher's chi-square (FCS) family, of which FCS test is the best. Applying FCS test to the real human HeLa cell-cycle dataset, degree distributions of the combined network is obtained and compared with previous works. Consulting the BioGRID database reveals the biological relevance of gene regulatory networks discovered using the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/186741
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 7th IEEE International Conference on Systems Biology (ISB 2013), Huangshan, China, 23-25 August 2013. In Conference Proceedings, 2013, p. 123-128en_US
dc.identifier.isbn978-1-4799-1389-3-
dc.identifier.urihttp://hdl.handle.net/10722/186741-
dc.description.abstractIdentifying regulatory genes partaking in disease development is important to medical advances. Since gene expression data of multiple experiments exist, combining results from multiple gene regulatory network discoveries offers higher sensitivity and specificity. However, data for multiple experiments on the same problem may not possess the same set of genes, and hence many existing combining methods are not applicable. In this paper, we approach this problem using a number of meta-analysis methods and compare their performances. Simulation results show that vote counting is outperformed by methods belonging to the Fisher's chi-square (FCS) family, of which FCS test is the best. Applying FCS test to the real human HeLa cell-cycle dataset, degree distributions of the combined network is obtained and compared with previous works. Consulting the BioGRID database reveals the biological relevance of gene regulatory networks discovered using the proposed method.-
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.subjectGene regulatory network-
dc.subjectMeta-analysis-
dc.subjectMultiple experiments-
dc.subjectPairwise Granger causality-
dc.subjectFisher's chi-square test-
dc.titleMeta-analysis on gene regulatory networks discovered by pairwise Granger causalityen_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.hkuros220049en_US
dc.identifier.spage123-
dc.identifier.epage128-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140103-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats