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

Bo Geng, Xiaobo Zhou, Y. S. Hung, Stephen T. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationComputational Models For Life Sciences (CMLS '07) - 2007 International Symposium
Pages279-286
Number of pages8
Volume952
DOIs
StatePublished - Dec 1 2007
Event2007 International Symposium on Computational Models for Life Sciences, CMLS '07 - Gold Coast, QLD, Australia
Duration: Dec 17 2007Dec 19 2007

Other

Other2007 International Symposium on Computational Models for Life Sciences, CMLS '07
CountryAustralia
CityGold Coast, QLD
Period12/17/0712/19/07

Keywords

  • Gene expression
  • Gene network growing
  • Metabolic reaction
  • Motif sequence

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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