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Multivariate Regression Applied to Gene Expression Dynamics

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Computational Intelligence in Bioinformatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 94))

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Summary

Linear multivariate regression tools developed on the basis of the traditional statistical theory are naturally suitable for high-dimensional data analysis. In this article, these methods are applied to microarray gene expression data. At first, a short introduction to dimension reduction techniques in both static and dynamic cases is given. After that, two examples, yeast cell response to environmental changes and expression during the cell cycle, are used to demonstrate the presented subspace identification method for data-based modeling of genome dynamics. The results show that the method is able to capture the relevant, higher level dynamical properties of the whole genome and can thus provide useful tools for intelligent data analysis. Especially the simplicity of the model structures leads to an easy interpretation of the obtained results.

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Author information

Authors and Affiliations

  1. Control Engineering Laboratory, Helsinki University of Technology, P.O.Box 5500, FI-02015 TKK, Finland

    Olli Haavisto & Heikki Hyötyniemi

Authors
  1. Olli Haavisto
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  2. Heikki Hyötyniemi
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Editor information

Editors and Affiliations

  1. Department of Neurology Buffalo Neuroimaging Analysis Center The Jacobs Neurological Institute University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14203, USA

    Arpad Kelemen

  2. Centre for Quantifiable Quality of Service in Communication Systems (Q2S), Centre of Excellence Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  3. School of Information Science and Engineering, Jinan University, Jiwei Road 106, Jinan, 250022, P.R. China

    Yuehui Chen

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© 2008 Springer-Verlag Berlin Heidelberg

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Haavisto, O., Hyötyniemi, H. (2008). Multivariate Regression Applied to Gene Expression Dynamics. In: Kelemen, A., Abraham, A., Chen, Y. (eds) Computational Intelligence in Bioinformatics. Studies in Computational Intelligence, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76803-6_11

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  • DOI: https://doi.org/10.1007/978-3-540-76803-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76802-9

  • Online ISBN: 978-3-540-76803-6

  • eBook Packages: EngineeringEngineering (R0)

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