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On finding and interpreting patterns in gene expression data from time course experiments

Pittelkow, Yvonne; Wilson, Susan

Description

Microarrays are being widely used for studying gene activity throughout a cell cycle. A common aim is to find those genes that are expressed during specific phases in the cycle. The challenges lie in the extremely large number of genes being measured simultaneously, the relatively short length of the time course studied and the high level of noise in the data. Using a well-known yeast cell cycle data set, we compare a method being used for finding genes following a periodic time series pattern...[Show more]

dc.contributor.authorPittelkow, Yvonne
dc.contributor.authorWilson, Susan
dc.date.accessioned2015-12-07T22:48:01Z
dc.identifier.isbn3540884343
dc.identifier.urihttp://hdl.handle.net/1885/26308
dc.description.abstractMicroarrays are being widely used for studying gene activity throughout a cell cycle. A common aim is to find those genes that are expressed during specific phases in the cycle. The challenges lie in the extremely large number of genes being measured simultaneously, the relatively short length of the time course studied and the high level of noise in the data. Using a well-known yeast cell cycle data set, we compare a method being used for finding genes following a periodic time series pattern with a method for finding genes having a different phase pattern during the cell cycle. Application of two visualisation tools gives insight into the interpretation of the patterns for the genes selected by the two approaches. It is recommended that (i) more than a single approach be used for finding patterns in gene expression data from time course experiments, and (ii) visualisation be used simultaneously with computational and statistical methods to interpret as well as display these patterns.
dc.publisherSpringer
dc.relation.ispartofPattern Recognition in Bioinformatics: proceedings of the 3rd International Workshop on Pattern Recognition in Bioinformatics
dc.relation.isversionof1st Edition
dc.subjectKeywords: Bioactivity; Bioinformatics; Feature extraction; Gene expression; Pattern recognition; Statistical methods; Visualization; Cell cycles; Gene activities; Gene expression datums; Phase patterns; Time courses; Time series patterns; Visualisation; Yeast cell
dc.titleOn finding and interpreting patterns in gene expression data from time course experiments
dc.typeBook chapter
local.description.notesImported from ARIES
dc.date.issued2008
local.identifier.absfor010110 - Partial Differential Equations
local.identifier.ariespublicationu9209279xPUB43
local.type.statusPublished Version
local.contributor.affiliationPittelkow, Yvonne, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationWilson, Susan, College of Physical and Mathematical Sciences, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage276
local.bibliographicCitation.lastpage287
local.identifier.doi10.1007/978-3-540-88436-1-24
dc.date.updated2016-02-24T11:54:49Z
local.bibliographicCitation.placeofpublicationBerlin, Germany
local.identifier.scopusID2-s2.0-57049126165
CollectionsANU Research Publications

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