A novel pattern based clustering methodology for time-series microarray data

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DOIResolve DOI: http://doi.org/10.1080/00207160701203419
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TypeArticle
Journal titleInternational Journal of Computer Mathematics
Volume84
Issue5
Pages585597; # of pages: 13
Subjectbiological pattern recognition; time-series microarray data; data mining; clustering; co-expressed genes
AbstractIdentification of co-expressed genes sharing similar biological behaviours is an essential step in functional genomics. Traditional clustering techniques are generally based on overall similarity of expression levels and often generate clusters with mixed profile patterns. A novel pattern recognition method for selecting co-expressed genes based on rate of change and modulation status of gene expression at each time interval is proposed in this paper. This method is capable of identifying gene clusters consisting of highly similar shapes of expression profiles and modulation patterns. Furthermore, we develop a quality index based on the semantic similarity in gene annotations to assess the likelihood of a cluster being a co-regulated group. The effectiveness of the proposed methodology is demonstrated by applying it to the well-known yeast sporulation dataset and an in-house cancer genomics dataset.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; Biotechnology Research Institute; National Research Council Canada
Peer reviewedNo
NRC number48820
NPARC number3539513
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Record identifieree5b248a-0399-4752-bf71-df4fa4b6914a
Record created2009-03-01
Record modified2016-05-09
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