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dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorSmith, Daviden_US
dc.contributor.authorWu, Shuanhuen_US
dc.contributor.authorXian, Junen_US
dc.contributor.authorYan, Hongen_US
dc.date.accessioned2017-04-04T21:28:55Z
dc.date.available2017-04-04T21:28:55Z
dc.date.issued2007en_US
dc.date.modified2009-10-16T05:19:57Z
dc.identifier.issn1471-2105en_US
dc.identifier.doi10.1186/1471-2105-8-137en_AU
dc.identifier.urihttp://hdl.handle.net/10072/17077
dc.description.abstractBackground: Periodogram analysis of time-series is widespread in biology. A new challenge for analyzing the microarray time series data is to identify genes that are periodically expressed. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, and unevenly sampled time points. Most methods used in the literature operate on evenly sampled time series and are not suitable for unevenly sampled time series. Results: For evenly sampled data, methods based on the classical Fourier periodogram are often used to detect periodically expressed gene. Recently, the Lomb-Scargle algorithm has been applied to unevenly sampled gene expression data for spectral estimation. However, since the Lomb-Scargle method assumes that there is a single stationary sinusoid wave with infinite support, it introduces spurious periodic components in the periodogram for data with a finite length. In this paper, we propose a new spectral estimation algorithm for unevenly sampled gene expression data. The new method is based on signal reconstruction in a shift-invariant signal space, where a direct spectral estimation procedure is developed using the B-spline basis. Experiments on simulated noisy gene expression profiles show that our algorithm is superior to the Lomb-Scargle algorithm and the classical Fourier periodogram based method in detecting periodically expressed genes. We have applied our algorithm to the Plasmodium falciparum and Yeast gene expression data and the results show that the algorithm is able to detect biologically meaningful periodically expressed genes. Conclusion: We have proposed an effective method for identifying periodic genes in unevenly sampled space of microarray time series gene expression data. The method can also be used as an effective tool for gene expression time series interpolation or resampling.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent53118 bytes
dc.format.extent1384410 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherBioMed Centralen_US
dc.publisher.placeUnited Kingdomen_US
dc.publisher.urihttp://www.biomedcentral.com/en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom1en_US
dc.relation.ispartofpageto19en_US
dc.relation.ispartofjournalBMC Bioinformaticsen_US
dc.relation.ispartofvolume8en_US
dc.rights.retentionNen_AU
dc.subject.fieldofresearchcode270201en_US
dc.subject.fieldofresearchcode280204en_US
dc.titleSpectral estimation in unevenly sampled space of periodically expressed microarray time series dataen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.description.notepublicPage numbers are not for citation purposes. Instead, this article has the unique article number of 137.en_AU
gro.rights.copyrightCopyright 2007 Liew et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_AU
gro.date.issued2007
gro.hasfulltextFull Text


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