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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© 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
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