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

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

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 unde...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohamed Salleh, Abdul Hakim, Mohammad, Mohd. Saberi
Format: Book Section
Published: Springer-Verlag. 2012
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/35807/
http://dx.doi.org/10.1007/978-3-642-32826-8_6
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Malaysia
id my.utm.35807
record_format eprints
spelling my.utm.358072017-02-02T05:55:05Z http://eprints.utm.my/id/eprint/35807/ Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models Mohamed Salleh, Abdul Hakim Mohammad, Mohd. Saberi Q Science (General) 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. Springer-Verlag. 2012 Book Section PeerReviewed Mohamed Salleh, Abdul Hakim and Mohammad, Mohd. Saberi (2012) Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models. In: Communications in Computer and Information Science. Springer-Verlag., Berlin, pp. 52-61. ISBN 978-364232825-1 http://dx.doi.org/10.1007/978-3-642-32826-8_6 DOI:10.1007/978-3-642-32826-8_6
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science (General)
spellingShingle Q Science (General)
Mohamed Salleh, Abdul Hakim
Mohammad, Mohd. Saberi
Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models
description 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.
format Book Section
author Mohamed Salleh, Abdul Hakim
Mohammad, Mohd. Saberi
author_facet Mohamed Salleh, Abdul Hakim
Mohammad, Mohd. Saberi
author_sort Mohamed Salleh, Abdul Hakim
title Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models
title_short Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models
title_full Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models
title_fullStr Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models
title_full_unstemmed Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models
title_sort identifying metabolic pathway within microarray gene expression data using combination of probabilistic models
publisher Springer-Verlag.
publishDate 2012
url http://eprints.utm.my/id/eprint/35807/
http://dx.doi.org/10.1007/978-3-642-32826-8_6
_version_ 1643649846363553792

Similar Items