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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-4929
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dc.contributor.authorHoward, Brian E.-
dc.contributor.authorSick, Beate-
dc.contributor.authorHeber, Steffen-
dc.date.accessioned2018-11-30T09:22:02Z-
dc.date.available2018-11-30T09:22:02Z-
dc.date.issued2009-
dc.identifier.issn1471-2105de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13394-
dc.description.abstractBackground: Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny. Results: We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach. Conclusion: This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations.de_CH
dc.language.isoende_CH
dc.publisherBioMed Centralde_CH
dc.relation.ispartofBMC Bioinformaticsde_CH
dc.rightshttp://creativecommons.org/licenses/by/2.0/de_CH
dc.subjectAlgorithmsde_CH
dc.subjectComputational biologyde_CH
dc.subjectGene expression profilingde_CH
dc.subjectNormal distributionde_CH
dc.subjectOligonucleotide array sequence analysisde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc570: Biologiede_CH
dc.titleUnsupervised assessment of microarray data quality using a Gaussian mixture modelde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.21256/zhaw-4929-
dc.identifier.doi10.1186/1471-2105-10-191de_CH
dc.identifier.pmid19545436de_CH
zhaw.funding.euNode_CH
zhaw.issue191de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume10de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Howard, B. E., Sick, B., & Heber, S. (2009). Unsupervised assessment of microarray data quality using a Gaussian mixture model. BMC Bioinformatics, 10(191). https://doi.org/10.21256/zhaw-4929
Howard, B.E., Sick, B. and Heber, S. (2009) ‘Unsupervised assessment of microarray data quality using a Gaussian mixture model’, BMC Bioinformatics, 10(191). Available at: https://doi.org/10.21256/zhaw-4929.
B. E. Howard, B. Sick, and S. Heber, “Unsupervised assessment of microarray data quality using a Gaussian mixture model,” BMC Bioinformatics, vol. 10, no. 191, 2009, doi: 10.21256/zhaw-4929.
HOWARD, Brian E., Beate SICK und Steffen HEBER, 2009. Unsupervised assessment of microarray data quality using a Gaussian mixture model. BMC Bioinformatics. 2009. Bd. 10, Nr. 191. DOI 10.21256/zhaw-4929
Howard, Brian E., Beate Sick, and Steffen Heber. 2009. “Unsupervised Assessment of Microarray Data Quality Using a Gaussian Mixture Model.” BMC Bioinformatics 10 (191). https://doi.org/10.21256/zhaw-4929.
Howard, Brian E., et al. “Unsupervised Assessment of Microarray Data Quality Using a Gaussian Mixture Model.” BMC Bioinformatics, vol. 10, no. 191, 2009, https://doi.org/10.21256/zhaw-4929.


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