Evolutionary Fuzzy Clustering Algorithm with Knowledge-Based Evaluation and Applications for Gene Expression Profiling
In microarray data analysis, clustering is a method that groups thousands of genes by their similarities of expression levels, helping to analyze gene expression profiles. This method has been used for identifying unknown functions of genes. The fuzzy clustering method assigns one sample to multiple groups according to their degrees of membership. This method is more appropriate for analyzing gene expression profiles, because a single gene might be involved in multiple functions. General clustering methods, however, have problems in that they are sensitive to initialization and can be trapped into local optima. To overcome these problems, we propose an evolutionary fuzzy clustering method with knowledge-based evaluation. The proposed method uses a genetic algorithm for clustering and prior knowledge of experimental data for evaluation. We have performed experiments to show the usefulness of the proposed method with yeast cell-cycle and SRBCT datasets.
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Document Type: Research Article
Publication date: December 1, 2005
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- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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