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

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Author Info

  • 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)

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    Abstract

    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.

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    Bibliographic Info

    Article provided by De Gruyter in its journal Statistical Applications in Genetics and Molecular Biology.

    Volume (Year): 8 (2009)
    Issue (Month): 1 (February)
    Pages: 1-34

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    Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:15

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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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