Algorithms For Approximation Proc, Chester 2005 by Iske A , Levesley J (Eds)

By Iske A , Levesley J (Eds)

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However, these successes can only be seen as the first step towards understanding the functions of genes and proteins and the interactions among cellular molecules. DNA microarray technologies provide an effective way to measure expression levels of tens of thousands of genes simultaneously under different conditions, which makes it possible to investigate gene activities of the whole genome [24, 53]. We demonstrate the applications of clustering algorithms in analyzing the explosively increasing gene expression data through both genes and tissues clustering.

Chan, T. Greiner, D. Weisenburger, J. Armitage, R. Warnke, R. Levy, W. Wilson, M. Grever, J. Byrd, D. Botstein, P. Brown, and L. Staudt: Distinct types of diffuse large B-cell Lymphoma identified by gene expression profiling. Nature, 2000, 503–511. 2. S. Altschul, W. Gish, W. Miller, E. Myers, and D. Lipman: Basic local alignment search tool. Journal of Molecular Biology, 1990, 403–410. 3. G. Anagnostopoulos and M. Georgiopoulos: Ellipsoid ART and ARTMAP for incremental unsupervised and supervised learning.

The software package GeneCluster, developed by Whitehead Institute/MIT Center for Genome Research, was used for SOFM analysis. In addition to genes clustering, tissues clustering are valuable in identifying samples that are in the different disease states, discovering or predicting different cancer types, and evaluating the effects of novel drugs and therapies [1, 31, 70]. Golub et al. described the restriction of traditional cancer classification methods and divided cancer classification as class discovery and class prediction.

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