Genetic algorithms applied to multi-class clustering for gene expression data.

Haiyan Pan; Jun Zhu; Danfu Han

doi:10.1016/s1672-0229(03)01033-7

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Genetic algorithms applied to multi-class clustering for gene expression data.

Haiyan Pan·Jun Zhu·Danfu Han

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Abstract

A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids. This schema maximized the clustering success by achieving internal cluster cohesion and external cluster isolation. The performance of HGACLUS and other methods was compared by using simulated data and open microarray gene-expression datasets. HGACLUS was generally found to be more accurate and robust than other methods discussed in this paper by the exact validation strategy and the explicit cluster number.

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Algorithms Cluster Analysis Computational Biology Computer Simulation Gene Expression Profiling Reproducibility of Results Sensitivity and Specificity Journal Article

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Haiyan Pan, Jun Zhu, Danfu Han. 2003. Genetic algorithms applied to multi-class clustering for gene expression data.. https://doi.org/10.1016/s1672-0229(03)01033-7

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