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.
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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
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DOI: https://doi.org/10.1007/978-3-540-85861-4_15
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