Clustering nodes in large-scale biological networks using external memory algorithms

Arefin, AS; Inostroza-Ponta, M; Mathieson, L; Berretta, R; Moscato, P

Clustering nodes in large-scale biological networks using external memory algorithms

Arefin, AS Inostroza-Ponta, M Mathieson, L

Berretta, R Moscato, P

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7017 LNCS (PART 2), pp. 375 - 386
Issue Date:: 2011-11-09

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Field	Value	Language
dc.contributor.author	Arefin, AS	en_US
dc.contributor.author	Inostroza-Ponta, M	en_US
dc.contributor.author	Mathieson, L https://orcid.org/0000-0001-6470-2296	en_US
dc.contributor.author	Berretta, R	en_US
dc.contributor.author	Moscato, P	en_US
dc.date.issued	2011-11-09	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7017 LNCS (PART 2), pp. 375 - 386	en_US
dc.identifier.isbn	9783642246685	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119970
dc.description.abstract	Novel analytical techniques have dramatically enhanced our understanding of many application domains including biological networks inferred from gene expression studies. However, there are clear computational challenges associated to the large datasets generated from these studies. The algorithmic solution of some NP-hard combinatorial optimization problems that naturally arise on the analysis of large networks is difficult without specialized computer facilities (i.e. supercomputers). In this work, we address the data clustering problem of large-scale biological networks with a polynomial-time algorithm that uses reasonable computing resources and is limited by the available memory. We have adapted and improved the MSTkNN graph partitioning algorithm and redesigned it to take advantage of external memory (EM) algorithms. We evaluate the scalability and performance of our proposed algorithm on a well-known breast cancer microarray study and its associated dataset. © 2011 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-24669-2_36	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Clustering nodes in large-scale biological networks using external memory algorithms	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	7017 LNCS	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	7017 LNCS	en_US

Abstract:

Novel analytical techniques have dramatically enhanced our understanding of many application domains including biological networks inferred from gene expression studies. However, there are clear computational challenges associated to the large datasets generated from these studies. The algorithmic solution of some NP-hard combinatorial optimization problems that naturally arise on the analysis of large networks is difficult without specialized computer facilities (i.e. supercomputers). In this work, we address the data clustering problem of large-scale biological networks with a polynomial-time algorithm that uses reasonable computing resources and is limited by the available memory. We have adapted and improved the MSTkNN graph partitioning algorithm and redesigned it to take advantage of external memory (EM) algorithms. We evaluate the scalability and performance of our proposed algorithm on a well-known breast cancer microarray study and its associated dataset. © 2011 Springer-Verlag.

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http://hdl.handle.net/10453/119970