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Conference Paper: Growing gene network by integration of gene expression, motif sequence and metabolic information
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TitleGrowing gene network by integration of gene expression, motif sequence and metabolic information
 
AuthorsGeng, B1 2
Zhou, X1
Hung, YS2
Wong, S1
 
KeywordsGene Expression
Gene Network Growing
Metabolic Reaction
Motif Sequence
 
Issue Date2007
 
CitationAip Conference Proceedings, 2007, v. 952, p. 279-286 [How to Cite?]
DOI: http://dx.doi.org/10.1063/1.2816632
 
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.
 
ISSN0094-243X
2012 SCImago Journal Rankings: 0.161
 
DOIhttp://dx.doi.org/10.1063/1.2816632
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorGeng, B
 
dc.contributor.authorZhou, X
 
dc.contributor.authorHung, YS
 
dc.contributor.authorWong, S
 
dc.date.accessioned2012-08-08T09:00:27Z
 
dc.date.available2012-08-08T09:00:27Z
 
dc.date.issued2007
 
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.
 
dc.description.natureLink_to_subscribed_fulltext
 
dc.identifier.citationAip Conference Proceedings, 2007, v. 952, p. 279-286 [How to Cite?]
DOI: http://dx.doi.org/10.1063/1.2816632
 
dc.identifier.doihttp://dx.doi.org/10.1063/1.2816632
 
dc.identifier.epage286
 
dc.identifier.issn0094-243X
2012 SCImago Journal Rankings: 0.161
 
dc.identifier.scopuseid_2-s2.0-71449108721
 
dc.identifier.spage279
 
dc.identifier.urihttp://hdl.handle.net/10722/158606
 
dc.identifier.volume952
 
dc.languageeng
 
dc.publisher.placeUnited States
 
dc.relation.ispartofAIP Conference Proceedings
 
dc.relation.referencesReferences in Scopus
 
dc.subjectGene Expression
 
dc.subjectGene Network Growing
 
dc.subjectMetabolic Reaction
 
dc.subjectMotif Sequence
 
dc.titleGrowing gene network by integration of gene expression, motif sequence and metabolic information
 
dc.typeConference_Paper
 
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<contributor.author>Hung, YS</contributor.author>
<contributor.author>Wong, S</contributor.author>
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Author Affiliations
  1. Cornell University
  2. The University of Hong Kong