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Identification of Gene Interaction Networks Based on Evolutionary Computation

  • Sung Hoon Jung
  • Kwang-Hyun Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3397)

Abstract

This paper investigates applying a genetic algorithm and an evolutionary programming for identification of gene interaction networks from gene expression data. To this end, we employ recurrent neural networks to model gene interaction networks and make use of an artificial gene expression data set from literature to validate the proposed approach. We find that the proposed approach using the genetic algorithm and evolutionary programming can result in better parameter estimates compared with the other previous approach. We also find that any a priori knowledge such as zero relations between genes can further help the identification process whenever it is available.

Keywords

Genetic Algorithm Gene Expression Data Bayesian Network Encode Scheme Boolean 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

  • Sung Hoon Jung
    • 1
  • Kwang-Hyun Cho
    • 2
    • 3
  1. 1.School of Information EngineeringHansung UniversitySeoulKorea
  2. 2.College of MedicineSeoul National UniversitySeoulKorea
  3. 3.Korea Bio-MAX InstituteSeoul National UniversitySeoulKorea

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