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Clustering microarray time-series data using expectation maximization and multiple profile alignment

Subhani, Numanul; Rueda, Luis; Ngom, Alioune; Burden, Conrad

Description

A common problem in biology is to partition a set of experimental data into clusters in such a way that the data points within the same cluster are highly similar while data points in different clusters are very different. In this direction, clustering microarray time-series data via pairwise alignment of piece-wise linear profiles has been recently introduced. We propose a EM clustering approach based on a multiple alignment of natural cubic spline representations of gene expression profiles....[Show more]

dc.contributor.authorSubhani, Numanul
dc.contributor.authorRueda, Luis
dc.contributor.authorNgom, Alioune
dc.contributor.authorBurden, Conrad
dc.coverage.spatialWashington, DC
dc.date.accessioned2015-12-13T22:53:19Z
dc.date.available2015-12-13T22:53:19Z
dc.date.createdNovember 1-4 2009
dc.identifier.isbn9781424451210
dc.identifier.urihttp://hdl.handle.net/1885/81758
dc.description.abstractA common problem in biology is to partition a set of experimental data into clusters in such a way that the data points within the same cluster are highly similar while data points in different clusters are very different. In this direction, clustering microarray time-series data via pairwise alignment of piece-wise linear profiles has been recently introduced. We propose a EM clustering approach based on a multiple alignment of natural cubic spline representations of gene expression profiles. The multiple alignment is achieved by minimizing the sum of integrated squared errors over a time-interval, defined on a set of profiles. Preliminary experiments on a well-known data set of 221 pre-clustered Saccharomyces cerevisiae gene expression profiles yield encouraging results with 83.26% accuracy.
dc.publisherIEEE
dc.relation.ispartofseries2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
dc.sourceProceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
dc.subjectKeywords: Common problems; Cubic spline; Data points; Data sets; EM clustering; Expectation Maximization; Experimental data; Gene expression profiles; Multiple alignment; Pairwise alignment; Piecewise linear; Profile alignment; Saccharomyces cerevisiae; Squared err Clustering; Cubic spline; Gene expression profiles; Microarrays; Profile alignment; Time-series data
dc.titleClustering microarray time-series data using expectation maximization and multiple profile alignment
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2009
local.identifier.absfor010402 - Biostatistics
local.identifier.absfor060102 - Bioinformatics
local.identifier.ariespublicationf5625xPUB10062
local.type.statusPublished Version
local.contributor.affiliationSubhani, Numanul, University of Windsor
local.contributor.affiliationRueda, Luis, University of Windsor
local.contributor.affiliationNgom, Alioune, University of Windsor
local.contributor.affiliationBurden, Conrad, College of Physical and Mathematical Sciences, ANU
local.bibliographicCitation.startpage2
local.bibliographicCitation.lastpage7
local.identifier.doi10.1109/BIBMW.2009.5332128
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciences
local.identifier.absseo970101 - Expanding Knowledge in the Mathematical Sciences
dc.date.updated2016-02-24T08:34:58Z
local.identifier.scopusID2-s2.0-72849121729
CollectionsANU Research Publications

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