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Evolution strategy applied to global optimization of clusters in gene expression data of DNA microarrays

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6 Author(s)
Kwonmoo Lee ; Bioinf. Lab., Samsung SDS, Seoul, South Korea ; Ju Han Kim ; Tae Su Chung ; Byoung-Sun Moon
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Cluster analysis is the most important method for analyzing large-scale gene expression patterns. The matrix representation of microarray data and its successive `optimal' incisional hyperplanes that create top-down hierarchical tree are a useful platform for developing optimization algorithms to determine the `optimal' clusters from a pairwise proximity matrix which represents completely connected and weighted graph. Evolution strategy is applied to determine the `globally optimal' incisional hyperplanes to construct hierarchical tree structure and tested with Fisher's iris and Golub's leukemia data sets. The results were compared with those of bottom-up hierarchical clustering, K-means and SOMs (Self-Organizing Maps) algorithms with promising results

Published in:

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on  (Volume:2 )

Date of Conference: