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)


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.


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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  1. 1.
    Reinitz, J., Sharp, D.H.: Mechanism of eve stripe formation. Mechanisms of Development, 133–158 (1995)Google Scholar
  2. 2.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. In: Proceedings of the National Academy of Sciences, pp. 14863–14868 (1998)Google Scholar
  3. 3.
    Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Pacific Symposium on Biocomputing, pp. 17–28 (1999)Google Scholar
  4. 4.
    Weaver, D., Workman, C., Stormo, G.: Modeling regulatory networks with weight matrices. In: Pacific Symposium on Biocomputing, pp. 112–123 (1999)Google Scholar
  5. 5.
    D’haeseleer, P., Wen, X., Fuhrman, S., Somogyi, R.: Linear modelling of mRNA expression levels during CNS development and injury. In: Pacific Symposium on Biocomputing, pp. 41–52 (1999)Google Scholar
  6. 6.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. Journal of Computational Biology, 601–620 (2000)Google Scholar
  7. 7.
    D’haeseleer, P.: Reconstructing Gene Networks from Large Scale Gene Expression Data. PhD thesis, University of New Mexico, Albuquerque (2000)Google Scholar
  8. 8.
    Wahde, M., Hertz, J.: Coarse-grained reverse engineering of genetic regulatory networks. BioSystems, 129–136 (2000)Google Scholar
  9. 9.
    van Someren, E., Wessels, L., Reinders, M.: Linear modelling of genetic networks from experimental data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 355–366 (2000)Google Scholar
  10. 10.
    van Someren, E., Wessels, L., Reinders, M.: Genetic network models: A comparative study. In: Proceedings of SPIE, Micro-arrays: Optical Technologies and Informatics, pp. 236–247 (2001)Google Scholar
  11. 11.
    Wessels, L., van Someren, E., Reinders, M.: A comparison of genetic network models. In: Pacific Symposium on Biocomputing, pp. 508–519 (2001)Google Scholar
  12. 12.
    Wahde, M., Hertz, J.: Modeling genetic regulatory dynamics in neural development. Journal of Computational Biology 8, 429–442 (2001)CrossRefGoogle Scholar
  13. 13.
    Takane, M.: Inference of Gene Regulatory Networks from Large Scale Gene Expression Data. Master’s thesis, McGill University (2003)Google Scholar
  14. 14.
    Chen, T., He, H.,, G.C.: Modeling gene expression with differential equations. In: Pacific Symposium on Biocomputing, pp. 29–40 (1999)Google Scholar
  15. 15.
    Holland, J.: Adaptation in natural and artificial systems, 1st, 2nd edn. University of Michigan Press, Ann Arbor (1992); MIT Press, Cambridge (1975)Google Scholar
  16. 16.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  17. 17.
    Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar
  18. 18.
    Fogel, D.B.: An Introduction to Simulated Evolutionary Optimization. IEEE Trans. on Neural Networks 5, 3–14 (1994)CrossRefGoogle Scholar

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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