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Tackling the DREAM Challenge for Gene Regulatory Networks Reverse Engineering

  • Alessia Visconti
  • Roberto Esposito
  • Francesca Cordero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6934)

Abstract

The construction and the understanding of Gene Regulatory Networks (GRNs) are among the hardest tasks faced by systems biology. The inference of a GRN from gene expression data (the GRN reverse engineering), is a challenging task that requires the exploitation of diverse mathematical and computational techniques. The DREAM conference proposes several challenges about the inference of biological networks and/or the prediction of how they are influenced by perturbations.

This paper describes a method for GRN reverse engineering that the authors submitted to the 2010 DREAM challenge. The methodology is based on a combination of well known statistical methods into a Naive Bayes classifier. Despite its simplicity the approach fared fairly well when compared to other proposals on real networks.

Keywords

Gene Regulatory Network Reverse Engineering Real Network Synthetic Dataset Probabilistic Graphical Model 
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 2011

Authors and Affiliations

  • Alessia Visconti
    • 1
    • 3
  • Roberto Esposito
    • 1
    • 3
  • Francesca Cordero
    • 1
    • 2
    • 3
  1. 1.Department of Computer ScienceUniversity of TorinoItaly
  2. 2.Department of Clinical and Biological SciencesUniversity of TorinoItaly
  3. 3.Interdepartmental Centre for Molecular Systems BiologyUniversity of TorinoItaly

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