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

Abdul Hakim Mohamed Salleh; Mohd Saberi Mohamad

doi:10.1007/978-3-642-32826-8_6

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 proceeding › Conference contribution

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.

Original language	English
Title of host publication	Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers
Pages	52-61
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-32826-8_6
Publication status	Published - 2012
Externally published	Yes
Event	3rd Knowledge Technology Week, KTW 2011 - Kajang, Malaysia Duration: Jul 18 2011 → Jul 22 2011

Publication series

Name	Communications in Computer and Information Science
Volume	295 CCIS
ISSN (Print)	1865-0929

Conference

Conference	3rd Knowledge Technology Week, KTW 2011
Country/Territory	Malaysia
City	Kajang
Period	7/18/11 → 7/22/11

Keywords

annotation
biological response
Metabolic pathway
probabilistic models

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-642-32826-8_6

Cite this

Mohamed Salleh, A. H., & Mohamad, M. S. (2012). Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models. In Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers (pp. 52-61). (Communications in Computer and Information Science; Vol. 295 CCIS). https://doi.org/10.1007/978-3-642-32826-8_6

Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models. / Mohamed Salleh, Abdul Hakim; Mohamad, Mohd Saberi.
Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers. 2012. p. 52-61 (Communications in Computer and Information Science; Vol. 295 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mohamed Salleh, AH & Mohamad, MS 2012, Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models. in Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers. Communications in Computer and Information Science, vol. 295 CCIS, pp. 52-61, 3rd Knowledge Technology Week, KTW 2011, Kajang, Malaysia, 7/18/11. https://doi.org/10.1007/978-3-642-32826-8_6

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this