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Identifying Metabolic Pathway within Microarray Gene Expression Data Using Combination of Probabilistic Models

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Knowledge Technology (KTW 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 295))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, UTM, 81310, Skudai, Johor, Malaysia

    Abdul Hakim Mohamed Salleh & Mohd Saberi Mohamad

Authors
  1. Abdul Hakim Mohamed Salleh
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  2. Mohd Saberi Mohamad
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Editor information

Editors and Affiliations

  1. MIMOS Berhad, Knowledge Technology, Technology Park Malaysia, 57000, Kuala Lumpur, Malaysia

    Dickson Lukose

  2. Department of Systems and Network,College of Information Technology, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43000, Kajang, Malaysia

    Abdul Rahim Ahmad  & Azizah Suliman  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Mohamed Salleh, A.H., Mohamad, M.S. (2012). Identifying Metabolic Pathway within Microarray Gene Expression Data Using Combination of Probabilistic Models. In: Lukose, D., Ahmad, A.R., Suliman, A. (eds) Knowledge Technology. KTW 2011. Communications in Computer and Information Science, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32826-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-32826-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32825-1

  • Online ISBN: 978-3-642-32826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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