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Finding the Optimal Gene Order in Displaying Microarray Data

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Book cover Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

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Abstract

The rapid advances of genome-scale sequencing have brought out the necessity of developing new data processing techniques for enormous genomic data. Microarrays, for example, can generate such a large number of gene expression data that we usually analyze them with some clustering algorithms. However, the clustering algorithms have been ineffective for visualization in that they are not concerned about the order of genes in each cluster. In this paper, a hybrid genetic algorithm for finding the optimal order of microarray data, or gene expression profiles, is proposed. We formulate our problem as a new type of traveling salesman problem and apply a hybrid genetic algorithm to the problem. To use the 2D natural crossover, we apply the Sammon’s mapping to the microarray data. Experimental results showed that our algorithm found improved gene orders for visualizing the gene expression profiles.

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Author information

Authors and Affiliations

  1. NHN Corp., 7th floor, Startower 737 Yoksam-dong, Kangnam-gu, Seoul, Korea

    Seung-Kyu Lee

  2. School of Computer Science & Engineering, Seoul National University, Shilim-dong, Kwanak-gu, Seoul, 151-742, Korea

    Yong-Hyuk Kim & Byung-Ro Moon

Authors
  1. Seung-Kyu Lee
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  2. Yong-Hyuk Kim
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  3. Byung-Ro Moon
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Editors and Affiliations

  1. Center for Applied Scientific Computing (CASC), Lawrence Livermore National Laboratory, 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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Lee, SK., Kim, YH., Moon, BR. (2003). Finding the Optimal Gene Order in Displaying Microarray Data. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_116

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  • DOI: https://doi.org/10.1007/3-540-45110-2_116

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

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