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Conference Paper: Growing gene network by integration of gene expression, motif sequence and metabolic information

TitleGrowing gene network by integration of gene expression, motif sequence and metabolic information
Authors
KeywordsGene Expression
Gene Network Growing
Metabolic Reaction
Motif Sequence
Issue Date2007
Citation
Aip Conference Proceedings, 2007, v. 952, p. 279-286 How to Cite?
Abstract
In computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological information makes it possible to improve gene network estimation. In this paper, we describe an approach for growing gene network from a sub-network by the integration of gene expression data, motif sequence, and metabolic information. To evaluate the approach, we apply it to a pool of E.coli genes related to aspartate pathway. The results show that integrative approach has potentials of reconstructing more accurate gene networks. © 2007 American Institute of Physics.
Persistent Identifierhttp://hdl.handle.net/10722/158606
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorGeng, Ben_US
dc.contributor.authorZhou, Xen_US
dc.contributor.authorHung, YSen_US
dc.contributor.authorWong, Sen_US
dc.date.accessioned2012-08-08T09:00:27Z-
dc.date.available2012-08-08T09:00:27Z-
dc.date.issued2007en_US
dc.identifier.citationAip Conference Proceedings, 2007, v. 952, p. 279-286en_US
dc.identifier.issn0094-243Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/158606-
dc.description.abstractIn computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological information makes it possible to improve gene network estimation. In this paper, we describe an approach for growing gene network from a sub-network by the integration of gene expression data, motif sequence, and metabolic information. To evaluate the approach, we apply it to a pool of E.coli genes related to aspartate pathway. The results show that integrative approach has potentials of reconstructing more accurate gene networks. © 2007 American Institute of Physics.en_US
dc.languageengen_US
dc.relation.ispartofAIP Conference Proceedingsen_US
dc.subjectGene Expressionen_US
dc.subjectGene Network Growingen_US
dc.subjectMetabolic Reactionen_US
dc.subjectMotif Sequenceen_US
dc.titleGrowing gene network by integration of gene expression, motif sequence and metabolic informationen_US
dc.typeConference_Paperen_US
dc.identifier.emailHung, YS:yshung@eee.hku.hken_US
dc.identifier.authorityHung, YS=rp00220en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1063/1.2816632en_US
dc.identifier.scopuseid_2-s2.0-71449108721en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-71449108721&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume952en_US
dc.identifier.spage279en_US
dc.identifier.epage286en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridGeng, B=25641387700en_US
dc.identifier.scopusauthoridZhou, X=8914487400en_US
dc.identifier.scopusauthoridHung, YS=8091656200en_US
dc.identifier.scopusauthoridWong, S=12781047500en_US

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