Bi-Phase Evolutionary Searching for Biclusters in Gene Expression Data

Huang, Q; Huang, X; Kong, Z; Li, X; Tao, D

Bi-Phase Evolutionary Searching for Biclusters in Gene Expression Data

Huang, Q Huang, X Kong, Z Li, X Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Evolutionary Computation, 2019, 23 (5), pp. 803 - 814
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Huang, Q	en_US
dc.contributor.author	Huang, X	en_US
dc.contributor.author	Kong, Z	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	IEEE Transactions on Evolutionary Computation, 2019, 23 (5), pp. 803 - 814	en_US
dc.identifier.issn	1089-778X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138349
dc.description.abstract	© 1997-2012 IEEE. The analysis of gene expression data is useful for detecting the biological information of genes. Biclustering of microarray data has been proposed as a powerful computational tool to discover subsets of genes that exhibit consistent expression patterns along subsets of conditions. In this paper, we propose a novel biclustering algorithm called the bi-phase evolutionary biclustering algorithm. The first phase is for the evolution of rows and columns, and the other is for the evolution of biclusters. The interaction of the two phases ensures a reliable search direction and accelerates the convergence to good solutions. Furthermore, the population is initialized using a conventional hierarchical clustering strategy to discover bicluster seeds. We also developed a seed-based parallel implementation of evolutionary searching to search biclusters more comprehensively. The performance of the proposed algorithm is compared with several popular biclustering algorithms using synthetic datasets and real microarray datasets. The experimental results show that the algorithm demonstrates a significant improvement in discovering biclusters.	en_US
dc.relation.ispartof	IEEE Transactions on Evolutionary Computation	en_US
dc.relation.isbasedon	10.1109/TEVC.2018.2884521	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Bi-Phase Evolutionary Searching for Biclusters in Gene Expression Data	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	23	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

© 1997-2012 IEEE. The analysis of gene expression data is useful for detecting the biological information of genes. Biclustering of microarray data has been proposed as a powerful computational tool to discover subsets of genes that exhibit consistent expression patterns along subsets of conditions. In this paper, we propose a novel biclustering algorithm called the bi-phase evolutionary biclustering algorithm. The first phase is for the evolution of rows and columns, and the other is for the evolution of biclusters. The interaction of the two phases ensures a reliable search direction and accelerates the convergence to good solutions. Furthermore, the population is initialized using a conventional hierarchical clustering strategy to discover bicluster seeds. We also developed a seed-based parallel implementation of evolutionary searching to search biclusters more comprehensively. The performance of the proposed algorithm is compared with several popular biclustering algorithms using synthetic datasets and real microarray datasets. The experimental results show that the algorithm demonstrates a significant improvement in discovering biclusters.

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http://hdl.handle.net/10453/138349