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Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis


  • Łuksza Marta

    (Max Planck Institute for Molecular Genetics)

  • Kluge Bogusław

    (University of Warsaw)

  • Ostrowski Jerzy

    (Maria Sklodowska-Curie Memorial Institute of Oncology)

  • Karczmarski Jakub

    (Maria Sklodowska-Curie Memorial Institute of Oncology)

  • Gambin Anna

    (University of Warsaw)


Proteomic mass spectrometry is gaining an increasing role in diagnostics and in studies on protein complexes and biological systems. This experimental technology is producing high-throughput data which is inherently noisy and may contain various errors. Mathematical processing can help in removing them.

Suggested Citation

  • Łuksza Marta & Kluge Bogusław & Ostrowski Jerzy & Karczmarski Jakub & Gambin Anna, 2009. "Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-34, February.
  • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:15

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    References listed on IDEAS

    1. Chris Fraley & Adrian E. Raftery, 2007. "Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 155-181, September.
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    Cited by:

    1. Melnykov, Volodymyr, 2013. "On the distribution of posterior probabilities in finite mixture models with application in clustering," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 175-189.

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