A memetic algorithm approach to network alignment: Mapping the classification of mental disorders of DSM-IV with ICD-10

Haque, MN; Mathieson, L; Moscato, P

A memetic algorithm approach to network alignment: Mapping the classification of mental disorders of DSM-IV with ICD-10

Haque, MN Mathieson, L

Moscato, P

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Publication Type:: Conference Proceeding
Citation:: GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019, pp. 258 - 265
Issue Date:: 2019-07-13

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Field	Value	Language
dc.contributor.author	Haque, MN	en_US
dc.contributor.author	Mathieson, L https://orcid.org/0000-0001-6470-2296	en_US
dc.contributor.author	Moscato, P	en_US
dc.date.issued	2019-07-13	en_US
dc.identifier.citation	GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019, pp. 258 - 265	en_US
dc.identifier.isbn	9781450361118	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137998
dc.description.abstract	© 2019 Association for Computing Machinery. Given two graphs modelling related, but possibly distinct, networks, the alignment of the networks can help identify signiicant structures and substructures which may relate to the functional purpose of the network components. The Network Alignment Problem is the NP-hard computational formalisation of this goal and is a useful technique in a variety of data mining and knowledge discovery domains. In this paper we develop a memetic algorithm to solve the Network Alignment Problem and demonstrate the efectiveness of the approach on a series of biological networks against the existing state of the art alignment tools. We also demonstrate the use of network alignment as a clustering and classiication tool on two mental health disorder diagnostic databases.	en_US
dc.relation.ispartof	GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference	en_US
dc.relation.isbasedon	10.1145/3321707.3321753	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	A memetic algorithm approach to network alignment: Mapping the classification of mental disorders of DSM-IV with ICD-10	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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	in_progress	*
pubs.publication-status	Published	en_US

Abstract:

© 2019 Association for Computing Machinery. Given two graphs modelling related, but possibly distinct, networks, the alignment of the networks can help identify signiicant structures and substructures which may relate to the functional purpose of the network components. The Network Alignment Problem is the NP-hard computational formalisation of this goal and is a useful technique in a variety of data mining and knowledge discovery domains. In this paper we develop a memetic algorithm to solve the Network Alignment Problem and demonstrate the efectiveness of the approach on a series of biological networks against the existing state of the art alignment tools. We also demonstrate the use of network alignment as a clustering and classiication tool on two mental health disorder diagnostic databases.

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http://hdl.handle.net/10453/137998