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dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorLaw, N.en_US
dc.contributor.authorYan, Hongen_US
dc.contributor.editorTuan Pham & Xiaobo Zhouen_US
dc.date.accessioned2017-04-24T12:52:40Z
dc.date.available2017-04-24T12:52:40Z
dc.date.issued2007en_US
dc.date.modified2009-10-16T05:20:03Z
dc.identifier.refurihttp://www.it.jcu.edu.au/~pham/CMLS07/CMLS07.htmen_AU
dc.identifier.doi10.1063/1.2816619en_AU
dc.identifier.urihttp://hdl.handle.net/10072/18030
dc.description.abstractMany cellular processes exhibit periodic behaviors. Hence, one of the important tasks in gene expression data analysis is to detect subset of genes that exhibit cyclicity or periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length and highly contaminated with noise. This makes detection of periodic profiles a very difficult problem. Recently, a hypothesis testing method based on the Fisher g-statistic with correction for multiple testing has been proposed to detect periodic gene expression profiles. However, it was observed that the test is not reliable if the signal length is too short. In this paper, we performed extensive simulation study to investigate the statistical power of the test as a function of signal length, SNR, and the false discovery rate. We found that the number of periodic profiles can be severely underestimated for short length signal. The findings indicated that caution needs to be exercised when interpreting the test result for very short length signals.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent411794 bytes
dc.format.extent20911 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherAmerican Institute of Physicsen_US
dc.publisher.placeUSAen_US
dc.publisher.urihttp://scitation.aip.org/dbt/dbt.jsp?KEY=APCPCS&Volume=952&Issue=1en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameInternational Symposium on Computational Models for Life Sciencesen_US
dc.relation.ispartofconferencetitleProceedings of the International Symposium on Computational Models for Life Sciencesen_US
dc.relation.ispartofdatefrom2007-12-17en_US
dc.relation.ispartofdateto2007-12-19en_US
dc.relation.ispartoflocationGold Coast, Australiaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode270201en_US
dc.subject.fieldofresearchcode280499en_US
dc.titleStatistical detection of short periodic gene expression time series profilesen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2007 American Institute of Physics. The attached file is reproduced here in accordance with the copyright policy of the publisher. Use hypertext link for access to the conference website.en_AU
gro.date.issued2007
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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