- Title
- A Memetic Algorithm Approach to Network Alignment: Mapping the Classification of Mental Disorders of DSM-IV with ICD-10
- Creator
- Haque, Mohammad Nazmul; Mathieson, Luke; Moscato, Pablo
- Relation
- GECCO 2019. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2019) (Prague, Czech Republic 13-17 July, 2019) p. 258-265
- Relation
- ARC.DP120102576 http://purl.org/au-research/grants/arc/DP120102576 DP140104183 http://purl.org/au-research/grants/arc/DP140104183 FT120100060 http://purl.org/au-research/grants/arc/FT120100060
- Publisher Link
- http://dx.doi.org/10.1145/3321707.3321753
- Publisher
- Association for Computing Machinery
- Resource Type
- conference paper
- Date
- 2019
- Description
- Given two graphs modelling related, but possibly distinct, networks, the alignment of the networks can help identify significant 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 effectiveness 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 classification tool on two mental health disorder diagnostic databases.
- Subject
- network alignment; memetic algorithm; graph theory; DSM-IV; ICD-10; protein-protein interaction network
- Identifier
- http://hdl.handle.net/1959.13/1446150
- Identifier
- uon:42778
- Identifier
- ISBN:9781450361118
- Language
- eng
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