Genetic Algorithms for Gene Expression Analysis

  • Ed Keedwell
  • Ajit Narayanan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)


The major problem for current gene expression analysis techniques is how to identify the handful of genes which contribute to a disease from the thousands of genes measured on gene chips (microarrays). The use of a novel neural-genetic hybrid algorithm for gene expression analysis is described here. The genetic algorithm identifies possible gene combinations for classification and then uses the output from a neural network to determine the fitness of these combinations. Normal mutation and crossover operations are used to find increasingly fit combinations. Experiments on artificial and real-world gene expression databases are reported. The results from the algorithm are also explored for biological plausibility and confirm that the algorithm is a powerful alternative to standard data mining techniques in this domain.


Genetic Algorithm Gene Expression Analysis Gene Expression Data Acute Myeloid Leukaemia Hybrid Algorithm 
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.
    Ando, S., Iba H.,. (2001a) “Inference of Gene Regulatory Model by Genetic Algorithms”, Proceedings of Conference on Evolutionary Computation 2001 pp712–719Google Scholar
  2. 2.
    Ando, S., Iba H., (2001b) “The Matrix Modeling of Gene Regulatory Networks-Reverse Engineering by Genetic Algorithms-”, Proceedings of Atlantic Symposium on Computational Biology, and Genome Information Systems & Technology 2001.Google Scholar
  3. 3.
    Fainstein E, Einat M, Gokkel E, Marcelle C, Croce CM, Gale RP, Canaani E (1989) “Nucleotide sequence analysis of human abl and bcr-abl cDNAs.” Oncogene 1989 Dec;4(12):1477–81Google Scholar
  4. 4.
    Golub, T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R.,. Caligiuri M.R., Bloomfield C.D., Lander, E.S. (1999) “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring” Science Vol286 pp531–536CrossRefGoogle Scholar
  5. 5.
    Narayanan, A., Keedwell, E. C. and Olsson, B. (2002). Artificial Intelligence techniques for Bioinformatics. Paper submitted to Applied Bioinformatics and available from
  6. 6.
    Page, D., Zhan, F., Cussens, J., Waddell, W., Hardin, J., Barlogie, B., Shaughnessy, J. Comparative Data Mining for Microarrays: A Case Study Based on Multiple Myeloma. Poster presentation at International Conference on Intelligent Systems for Molecular Biology August 3-7, Edmonton, Canada. Technical report available from mwaddell@biostat.wisc.eduGoogle Scholar
  7. 7.
    Quinlan, J.R. (1993) C4.5: Programs for Machine Learning Morgan Kaufmann Publishers.Google Scholar
  8. 8.
    Ryu, J., Sung-Bae, C., (2002) “Gene expression classification using optimal feature/classifier ensemble with negative correlation” Proceedings of the International Joint Conference on Neural Networks (IJCNN’02), Honolulu, Hawaii, pp 198–203, ISBN 0-7803-7279-4Google Scholar
  9. 9.
    Shilatifard A, Duan DR, Haque D, Florence C, Schubach WH, Conaway JW, Conaway RC. (1997) “ELL2, a new member of an ELL family of RNA polymerase II elongation factors.” Proc Natl Acad Sci USA 1997 Apr 15;94(8):3639–43Google Scholar
  10. 10.
    Simmons D, Seed B. (1988) “Isolation of a cDNA encoding CD33, a differentiation antigen of myeloid progenitor cells.” Journal of Immunology 1988 Oct 15;141(8):2797–800Google Scholar
  11. 11.
    Su, T., Basu, M., Toure, A., (2002) “Multi-Domain Gating Network for Classification of Cancer Cells using Gene Expression Data” Proceedings of the International Joint Conference on Neural Networks (IJCNN’02), Honolulu, Hawaii, pp 286–289, ISBN 0-7803-7279-4Google Scholar
  12. 12.
    Winston, P. H. (1992). Artificial Intelligence (3rd Edition). Addison Wesley.Google Scholar
  13. 13.
    Xu R., Anagnostopoulos G., Wunsch II D.C., “Tissue Classification Through Analysis of Gene Expression Data Using A New Family of ART Architectures”, Proceedings on International Joint Conference on Neural Networks, Honolulu, Hawaii, Vol. 1, pp. 300–304, May 2002.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ed Keedwell
    • 1
  • Ajit Narayanan
    • 1
  1. 1.School of Engineering and Computer ScienceUniversity of ExeterExeter

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