A Matrix Factorization Classifier for Knowledge-Based Microarray Analysis

  • R. Schachtner
  • D. Lutter
  • A. M. Tomé
  • G. Schmitz
  • P. Gómez Vilda
  • E. W. Lang
Part of the Advances in Soft Computing book series (AINSC, volume 49)


In this study we analyze microarray data sets which monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that matrix decomposition techniques are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles. With these marker genes corresponding test data sets can then easily be classified into related diagnostic categories. The latter correspond to either monocytes vs macrophages or healthy vs Niemann Pick C diseased patients. Our results demonstrate that these methods are able to identify suitable marker genes which can be used to classify the type of cell lines investigated.


Independent Component Analysis Matrix Factorization Independent Component Analysis Gene Expression Signature Nonnegative Matrix Factorization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • R. Schachtner
    • 1
  • D. Lutter
    • 2
  • A. M. Tomé
    • 3
  • G. Schmitz
    • 4
  • P. Gómez Vilda
    • 5
  • E. W. Lang
    • 1
  1. 1.CIML/BiophysicsUniversity of RegensburgRegensburgGermany
  2. 2.CMB/IBI, GSFMunichGermany
  3. 3.IEETA/DETIUniversidade de AveiroAveiroPortugal
  4. 4.Clinical ChemistryUniversity Hospital RegensburgRegensburgGermany
  5. 5.P. Gómez Vilda, DATSI/FIUniversidad Politécnica de MadridMadridSpain

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