Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models

Abdul Hakim Mohamed Salleh, Mohd Saberi Mohamad

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


Extracting metabolic pathway that dictates a specific biological response is currently one of the important disciplines in metabolic system biology research. Previous methods have successfully identified those pathways but without concerning the genetic effect and relationship of the genes, the underlying structure is not precisely represented and cannot be justified to be significant biologically. In this article, probabilistic models capable of identifying the significant pathways through metabolic networks that are related to a specific biological response are implemented. This article utilized combination of two probabilistic models, using ranking, clustering and classification techniques to address limitations of previous methods with the annotation to Kyoto Encyclopedia of Genes and Genomes (KEGG) to ensure the pathways are biologically plausible.

Original languageEnglish
Title of host publicationKnowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers
Number of pages10
Publication statusPublished - 2012
Externally publishedYes
Event3rd Knowledge Technology Week, KTW 2011 - Kajang, Malaysia
Duration: Jul 18 2011Jul 22 2011

Publication series

NameCommunications in Computer and Information Science
Volume295 CCIS
ISSN (Print)1865-0929


Conference3rd Knowledge Technology Week, KTW 2011


  • annotation
  • biological response
  • Metabolic pathway
  • probabilistic models

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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