Graph partitioning algorithms for detecting functional module from yeast protein interaction network

Advances in high-throughput technologies have provided many opportunities for researchers to study and better understand the dynamic mechanisms of systems biology. These systems are frequently formed by a functional organisation of networks that recapitulate specific biological processes. Protein in...

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Main Author:	Abdullah, Afnizanfaizal
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11570/1/AfnizanFaizalAbdullahMFSKSM2010.pdf
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spelling	my-utm-ep.115702018-05-30T02:49:07Z Graph partitioning algorithms for detecting functional module from yeast protein interaction network 2010-04 Abdullah, Afnizanfaizal QA75 Electronic computers. Computer science Advances in high-throughput technologies have provided many opportunities for researchers to study and better understand the dynamic mechanisms of systems biology. These systems are frequently formed by a functional organisation of networks that recapitulate specific biological processes. Protein interaction networks contain sets of sub-networks called functional modules with highly interactive proteins that perform similar functions. Recently, many graph partitioning algorithms have been proposed for detecting these modules, focusing only on detecting highly interactive proteins and neglecting proteins participating in sparse interactions. Moreover, many algorithms do not consider the overlap among different modules when identifying proteins that perform more than one function. In this research, new graph partitioning algorithms called Reliable Local Dense Neighbourhood (RELODEN) and Overlap-RELODEN are proposed to detect modules that contain highly interactive proteins, while also considering proteins with sparse interaction and overlap between different modules. The algorithms are based on the clique finding approach, which searches local cliques of informative proteins and groups the cliques into larger sub-networks. Experimental analyses using budding yeast (Saccharomyces cerevisiae) protein interaction network have shown that the proposed algorithms have the capability of detecting modules that are significant to biological functions, and thus giving a higher accuracy performance compared with existing algorithms. Moreover, these algorithms have found several interactive proteins that have not been reported previously, and are able to potentially predict the functions of a number of uncategorised proteins. 2010-04 Thesis http://eprints.utm.my/id/eprint/11570/ http://eprints.utm.my/id/eprint/11570/1/AfnizanFaizalAbdullahMFSKSM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Abdullah, Afnizanfaizal Graph partitioning algorithms for detecting functional module from yeast protein interaction network
description	Advances in high-throughput technologies have provided many opportunities for researchers to study and better understand the dynamic mechanisms of systems biology. These systems are frequently formed by a functional organisation of networks that recapitulate specific biological processes. Protein interaction networks contain sets of sub-networks called functional modules with highly interactive proteins that perform similar functions. Recently, many graph partitioning algorithms have been proposed for detecting these modules, focusing only on detecting highly interactive proteins and neglecting proteins participating in sparse interactions. Moreover, many algorithms do not consider the overlap among different modules when identifying proteins that perform more than one function. In this research, new graph partitioning algorithms called Reliable Local Dense Neighbourhood (RELODEN) and Overlap-RELODEN are proposed to detect modules that contain highly interactive proteins, while also considering proteins with sparse interaction and overlap between different modules. The algorithms are based on the clique finding approach, which searches local cliques of informative proteins and groups the cliques into larger sub-networks. Experimental analyses using budding yeast (Saccharomyces cerevisiae) protein interaction network have shown that the proposed algorithms have the capability of detecting modules that are significant to biological functions, and thus giving a higher accuracy performance compared with existing algorithms. Moreover, these algorithms have found several interactive proteins that have not been reported previously, and are able to potentially predict the functions of a number of uncategorised proteins.
format	Thesis
qualification_level	Master's degree
author	Abdullah, Afnizanfaizal
author_facet	Abdullah, Afnizanfaizal
author_sort	Abdullah, Afnizanfaizal
title	Graph partitioning algorithms for detecting functional module from yeast protein interaction network
title_short	Graph partitioning algorithms for detecting functional module from yeast protein interaction network
title_full	Graph partitioning algorithms for detecting functional module from yeast protein interaction network
title_fullStr	Graph partitioning algorithms for detecting functional module from yeast protein interaction network
title_full_unstemmed	Graph partitioning algorithms for detecting functional module from yeast protein interaction network
title_sort	graph partitioning algorithms for detecting functional module from yeast protein interaction network
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/11570/1/AfnizanFaizalAbdullahMFSKSM2010.pdf
_version_	1747814871162945536

Graph partitioning algorithms for detecting functional module from yeast protein interaction network

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