Functional Classification of Genes Using Non-Negative Independent Component Analysis

  • Monica Chagoyen
  • Hugo Fernandes
  • Jose M. Carazo
  • Alberto Pascual-Montano
Conference paper
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 12)

In the last few years, several analysis methods have been proposed to assist in the functional interpretation of genome-wide data. To this aim, we explore the use of non-negative Independent Component Analysis (nnICA) for the classifi- cation of genes based on their associated functional annotations.


Gene Ontology Functional Annotation Independent Component Analysis Blind Signal Latent Semantic Indexing 
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 2008

Authors and Affiliations

  • Monica Chagoyen
    • 1
    • 3
  • Hugo Fernandes
    • 2
  • Jose M. Carazo
    • 1
  • Alberto Pascual-Montano
    • 3
  1. 1.Biocomputing UnitCentro Nacional de Biotecnologia - CSICMadridSpain
  2. 2.Integromics S.L.GranadaSpain
  3. 3.Biocomputing UnitCentro Nacional de Biotecnologia - CSICMadridSpain

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