Bayesian Networks Learning for Gene Expression Datasets

  • Giacomo Gamberoni
  • Evelina Lamma
  • Fabrizio Riguzzi
  • Sergio Storari
  • Stefano Volinia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3646)


DNA arrays yield a global view of gene expression and can be used to build genetic networks models, in order to study relations between genes. Literature proposes Bayesian network as an appropriate tool for develop similar models. In this paper, we exploit the contribute of two Bayesian network learning algorithms to generate genetic networks from microarray datasets of experiments performed on Acute Myeloid Leukemia (AML).

In the results, we present an analysis protocol used to synthesize knowledge about the most interesting gene interactions and compare the networks learned by the two algorithms. We also evaluated relations found in these models with the ones found by biological studies performed on AML.


Acute Myeloid Leukemia Bayesian Network Association Rule Gene Expression Dataset Causal Network 
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 2005

Authors and Affiliations

  • Giacomo Gamberoni
    • 1
  • Evelina Lamma
    • 1
  • Fabrizio Riguzzi
    • 1
  • Sergio Storari
    • 1
  • Stefano Volinia
    • 2
  1. 1.ENDIF-Dipartimento di IngegneriaUniversità di FerraraFerraraItaly
  2. 2.Dipartimento di BiologiaUniversità di FerraraFerraraItaly

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