Subspace clustering of DNA microarray data: theory, evaluation, and applications

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DOIResolve DOI: http://doi.org/10.4018/IJCMAM.2014070101
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TypeArticle
Journal titleInternational Journal of Computational Models and Algorithms in Medicine
ISSN1947-3133
1947-3141
Volume4
Issue2
Pages152
AbstractIdentification of biological significant subspace clusters (biclusters and triclusters) of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Several methods and applications of subspace clustering (biclustering and triclustering) in DNA microarray data analysis have been developed in recent years. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. This review discusses and compares these methods, highlights their mathematical principles, and provides insight into the applications to solve biological problems.
Publication date
PublisherIGI Global
LanguageEnglish
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number23002083
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Record identifier6c6034d3-9b03-4ee9-a06d-f72ea72391fc
Record created2017-08-10
Record modified2017-08-10
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