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Stochastic Algorithms for Gene Expression Analysis

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Stochastic Algorithms: Foundations and Applications (SAGA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2827))

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Abstract

Recent advances in the measurement of gene expression have allowed large data sets to become available for different types of analyses. In these data sets, the number of variables exceeds the number of observations by at least one order of magnitude. Substantial variable reduction is usually necessary before learning algorithms can be utilized in practice. Commonly used greedy variable selection strategies preclude the discovery of potentially important variable combinations if the variables in the combination are not sufficiently informative in isolation. Given the high dimensionality, artifacts are frequent and the use of evaluation techniques to prevent model overfitting need to be employed. In this article, we describe the factors that make the analysis of high-throughput gene expression data especially challenging, and indicate why properly evaluated stochastic algorithms can play a particularly important role in this process.

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Author information

Authors and Affiliations

  1. Decision Systems Group, Division of Health Science and Technology, Harvard-MIT, Cambridge, USA

    Lucila Ohno-Machado & Winston Patrick Kuo

  2. Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, USA

    Winston Patrick Kuo

Authors
  1. Lucila Ohno-Machado
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  2. Winston Patrick Kuo
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Editor information

Editors and Affiliations

  1. Science and Technology Research Institute, University of Hertfordshire, AL10 9AB, Hatfield, Herts, UK

    Andreas Albrecht

  2. Department of Computer Science, King’s College London, WC2R 2LS, London, UK

    Kathleen Steinhöfel

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© 2003 Springer-Verlag Berlin Heidelberg

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Ohno-Machado, L., Kuo, W.P. (2003). Stochastic Algorithms for Gene Expression Analysis. In: Albrecht, A., Steinhöfel, K. (eds) Stochastic Algorithms: Foundations and Applications. SAGA 2003. Lecture Notes in Computer Science, vol 2827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39816-5_4

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  • DOI: https://doi.org/10.1007/978-3-540-39816-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20103-8

  • Online ISBN: 978-3-540-39816-5

  • eBook Packages: Springer Book Archive

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