Summary
In this paper we propose a novel hierarchical clustering method that uses a genetic algorithm based on mathematical proofs for the analysis of gene expression data, and show its effectiveness with regard to other clustering methods. The analysis of clusters with genetic algorithms has disclosed good results on biological data, and several studies have been carried out on the latter, although the majority of these researches have been focused on the partitional approach. On the other hand, the deterministic methods for hierarchical clustering generally converge to a local optimum. The method introduced here attempts to solve some of the problems faced by other hierarchical methods. The results of the experiments show that the method could be very effective in the cluster analysis on DNA microarray data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley Longman, Inc. (1989)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1999)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1998)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data. An Introduction to Clustering Analysis. John Wiley & Sons, Inc., Hoboken (2005)
Eisen, M., Spellman, T., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, USA 95, 14863–14868 (1998)
Jiang, D., Pei, J., Zhang, A.: DHC: A density-based hierarchical clustering method for time series gene expression data. In: Proceedings of the Third IEEE Symposium on BioInformatics and BioEngineering (BIBE) (2003)
Berrar, D.P., Dubitzky, W., Granzow, M.: A Practical Approach to Microarray Data Analysis. Kluwer Academic Publishers, New York (2003)
Speed, T.: Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC Press LLC (2003)
De-Jong, K.A., Spears, W.M.: Using Genetic Algorithms to Solve NP-Complete Problems. In: Proceedings of the Third International Conference on Genetic Algorithms (1989)
Godefriud, P., Khurshid, S.: Exploring very large state spaces using genetic algorithms. In: Katoen, J.-P., Stevens, P. (eds.) TACAS 2002. LNCS, vol. 2280, pp. 266–280. Springer, Heidelberg (2002)
Chu, P.C., Beasley, J.E.: A genetic algorithm for the set partitioning problem. Technical report, Imperial College, The Management School, London, England, 481–487 (1995)
Maulik, U., Bandyopadhyay, S.: Genetic algorithms-based clustering technique. The Journal of the Pattern Recognition Society 33, 1455–1465 (2000)
Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: A survey. IEEE Transactions on Knowledge and Data Engineering 16(11), 1370–1386 (2004)
Greene, W.A.: Unsupervised hierarchical clustering via a genetic algorithm. In: IEEE Congress on Evolutionary Computation, CEC 2003, vol. 2, pp. 998–1005 (2003)
Castellanos-Garzón, J.A., Miguel-Quintales, L.A.: Algoritmos genáticos para clustering de datos de expresión génica. Master’s thesis, Computer Science and Automatic Department, University of Salamanca, Spain (2006)
Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis 21, 3201–3212 (2005)
Yee-Yeung, K.: Clustering Analysis of Gene Expression Data. PhD thesis, University of Washintong (2001)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2006) ISBN 3-900051-07-0
Chipman, H., Tibshirani, R.: Hybrid hierarchical clustering with applications to microarray data. Biostatistics 7, 302–317 (2006)
Macnaughton-Smith, P., Williams, W.T., Dale, M.B., Mockett, L.G.: Dissimilarity analysis: a new technique of hierarchical subdivision. Nature 202, 1034–1035 (1965)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Castellanos-Garzón, J.A., Miguel-Quintales, L.A. (2009). Evolutionary Techniques for Hierarchical Clustering Applied to Microarray Data. In: Corchado, J.M., De Paz, J.F., Rocha, M.P., Fernández Riverola, F. (eds) 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85861-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-85861-4_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85860-7
Online ISBN: 978-3-540-85861-4
eBook Packages: EngineeringEngineering (R0)