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Interpreting Microarray Experiments Via Co-expressed Gene Groups Analysis (CGGA)

  • Ricardo Martinez
  • Nicolas Pasquier
  • Claude Pasquier
  • Lucero Lopez-Perez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)

Abstract

Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of huge data using different sources of information.

We propose a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed.

CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology (GO).

By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments.

Keywords

Gene Ontology Microarray Experiment Aerobic Respiration Respiratory Chain Complex Filamentous Growth 
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 2006

Authors and Affiliations

  • Ricardo Martinez
    • 1
  • Nicolas Pasquier
    • 1
  • Claude Pasquier
    • 2
  • Lucero Lopez-Perez
    • 3
  1. 1.Laboratoire I3SSophia-AntipolisFrance
  2. 2.Laboratoire Biologie VirtuelleCentre de Biochimie, Parc ValroseNiceFrance
  3. 3.INRIA Sophia AntipolisSophia-AntipolisFrance

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