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Validating clusterings of gene expression data

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Abstract
We propose a measure for the validation of clusterings of gene expression data. This measure also useful to estimate missing gene expression levels, based the similarity information contained in a given clustering. It is shown that this measure is an improvement over the figure of merit, an existing validation measure especially developed for clusterings of gene expression data.
Keywords
genetics, bioinformatics, pattern clustering, clustering validation, gene expression data, missing gene expression levels, similarity information, validation measure

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Please use this url to cite or link to this publication:

MLA
De Mulder, Wim, et al. “Validating Clusterings of Gene Expression Data.” 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), vol. 1, IEEE, 2010, pp. 241–45.
APA
De Mulder, W., Kuiper, M., & Boel, R. (2010). Validating clusterings of gene expression data. In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010) (Vol. 1, pp. 241–245). Piscataway, NJ, USA: IEEE.
Chicago author-date
De Mulder, Wim, Martin Kuiper, and René Boel. 2010. “Validating Clusterings of Gene Expression Data.” In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), 1:241–45. Piscataway, NJ, USA: IEEE.
Chicago author-date (all authors)
De Mulder, Wim, Martin Kuiper, and René Boel. 2010. “Validating Clusterings of Gene Expression Data.” In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), 1:241–245. Piscataway, NJ, USA: IEEE.
Vancouver
1.
De Mulder W, Kuiper M, Boel R. Validating clusterings of gene expression data. In: 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). Piscataway, NJ, USA: IEEE; 2010. p. 241–5.
IEEE
[1]
W. De Mulder, M. Kuiper, and R. Boel, “Validating clusterings of gene expression data,” in 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), Singapore, Singapore, 2010, vol. 1, pp. 241–245.
@inproceedings{1191851,
  abstract     = {We propose a measure for the validation of clusterings of gene expression data. This measure also useful to estimate missing gene expression levels, based the similarity information contained in a given clustering. It is shown that this measure is an improvement over the figure of merit, an existing validation measure especially developed for clusterings of gene expression data.},
  author       = {De Mulder, Wim and Kuiper, Martin  and Boel, René},
  booktitle    = {2nd International Conference on Computer and Automation Engineering (ICCAE 2010)},
  isbn         = {9781424455850},
  keywords     = {genetics,bioinformatics,pattern clustering,clustering validation,gene expression data,missing gene expression levels,similarity information,validation measure},
  language     = {eng},
  location     = {Singapore, Singapore},
  pages        = {241--245},
  publisher    = {IEEE},
  title        = {Validating clusterings of gene expression data},
  url          = {http://dx.doi.org/10.1109/ICCAE.2010.5451960},
  volume       = {1},
  year         = {2010},
}

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