Advertisement

Clustering of Gene Expression Data with Quantum-Behaved Particle Swarm Optimization

  • Wei Chen
  • Jun Sun
  • Yanrui Ding
  • Wei Fang
  • Wenbo Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

Based on the previously proposed Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, in this paper, we focus on the application of QPSO in gene expression data clustering which can be reduced to an optimization problem. The proposed clustering algorithm partitions the N patterns of the gene expression dataset into user-defined K categories to minimize the fitness function of Total Within-Cluster Variation. Thus a partition with high performance is obtained. The experiment results on four gene expression data sets show that our QPSO-based clustering algorithm will be an effective and promising tool for gene expression data analysis.

Keywords

Cluster Algorithm Gene Expression Data Particle Swarm Optimization Algorithm Rand Index Gene Expression Dataset 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shamir, R., Sharan, R.: Approaches to clustering gene expression data. In: Jiang, T., Smith, T., Xu, Y., Zhang, M.Q. (eds.) Current Topics in Computational Biology, MIT press, Cambridge (2001)Google Scholar
  2. 2.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14 863–14 868Google Scholar
  3. 3.
    Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecture. Nat. Genet. 22, 281–285 (1999)CrossRefGoogle Scholar
  4. 4.
    Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R.: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96, 2907–2912Google Scholar
  5. 5.
    Krishna, K., Murty, M.: Genetic K-means algorithm. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 29, 433–439 (1999)CrossRefGoogle Scholar
  6. 6.
    Lu, Y., Lu, S., Fotouhi, F., Deng, Y., Brown, S.: FGKA: A Fast Genetic K-means Algorithm, March 2004 (2004)Google Scholar
  7. 7.
    Lu, Y., Lu, S., Fotouhi, F., Deng, Y., Brown, S.: Fast genetic K-means algorithm and its application in gene expression data analysis. Wayne State University, Detroit (2003)Google Scholar
  8. 8.
    Lu, Y., Lu, S., Fotouhi, F., Deng, Y., Brown, S.: Incremental genetic K-means algorithm and its application in gene expression data analysis. BMC Bioinformatics 5, 172 (2004)CrossRefGoogle Scholar
  9. 9.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE int. Conf. On Neural Network, pp. 1942–1948 (1995)Google Scholar
  10. 10.
    van der Merwe, D.W., Engelbrecht, A.P.: Data Clustering using Particle Swarm Optimization[J/OL]. In: Proc. 2003 Congress on Evolutionary Computation, Piscataway, NJ, pp. 215–220 (2003)Google Scholar
  11. 11.
    Sun, J., Feng, B., Xu, W.-B.: Particle Swarm Optimization with Particles Having Quantum Behavior. In: Proc. 2004 Congress on Evolutionary Computation, Piscataway, NJ, pp. 325–331 (2004)Google Scholar
  12. 12.
    Sun, J., Xu, W.-B., Feng, B.: A Global Search Strategy of Quantum-behaved Particle Swarm Optimization. In: Proc. 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, pp. 111–116 (2004)Google Scholar
  13. 13.
    Sun, J., Xu, W.-B., Feng, B.: Adaptive Parameter Control for Quantum-behaved Particle Swarm Optimization on Individual Level. In: Proc. 2005 IEEE International Conference on Systems, Man and Cybernetics, Piscataway, NJ, pp. 3049–3054 (2005)Google Scholar
  14. 14.
    Wen, X.L., Fuhrman, S., Michaels, G.S., et al.: Large-scale temporal gene expression mapping of central nervous system development. Proc Natl. Acad. Sci. USA 95(1), 334–339 (1998)CrossRefGoogle Scholar
  15. 15.
    Ideker, T., Thorsson, V., Ranish, J.A., et al.: Integrated genomic and proteomic analyses of a systemically perturbed metabolic network. Science 292(5518), 929–943 (2001)CrossRefGoogle Scholar
  16. 16.
    Yeung, K.Y., Medvedovic, M., Bumgarner, R.E.: Clustering gene expression data with repeated measurements. Genome Biology 4(5), R34 (2003)CrossRefGoogle Scholar
  17. 17.
    Cho, R.J., Campbell, M.J., Winzeler, E.A., et al.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2(1), 65–73 (1998)CrossRefGoogle Scholar
  18. 18.
    Yeung, K.Y., Haynor, D.R., Ruzzo, W.: Validating clustering for gene expression data. Bioinformatics 17(4), 309–318 (2001)CrossRefGoogle Scholar
  19. 19.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classification, 193–218 (1985)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wei Chen
    • 1
  • Jun Sun
    • 1
  • Yanrui Ding
    • 1
  • Wei Fang
    • 1
  • Wenbo Xu
    • 1
  1. 1.School of Information TechnologyJiangnan UniversityJiangsuP.R. China

Personalised recommendations