Gene Network Reconstruction Using a Distributed Genetic Algorithm with a Backprop Local Search

  • Mark Cumiskey
  • John Levine
  • Douglas Armstrong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)


With the first draft completion of multiple organism genome sequencing programmes the emphasis is now moving toward a functional understanding of these genes and their network interactions. Microarray technology allows for large-scale gene experimentation. Using this technology it is possible to find the expression levels of genes across different conditions. The use of a genetic algorithm with a backpropagation local searching mechanism to reconstruct gene networks was investigated. This study demonstrates that the distributed genetic algorithm approach shows promise in that the method can infer gene networks that fit test data closely. Evaluating the biological accuracy of predicted networks from currently available test data is not possible. The best that can be achieved is to produce a set of possible networks to pass to a biologist for experimental verification.


Genetic Algorithm Local Search Gene Network Single Machine Weight Setting 
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 2003

Authors and Affiliations

  • Mark Cumiskey
    • 1
  • John Levine
    • 1
  • Douglas Armstrong
    • 1
  1. 1.School of InformaticsUniversity of EdinburghEdinburghScotland

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