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Robust Perron Cluster Analysis for Various Applications in Computational Life Science

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Computational Life Sciences (CompLife 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3695))

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Abstract

In the present paper we explain the basic ideas of Robust Perron Cluster Analysis (PCCA+) and exemplify the different application areas of this new and powerful method. Recently, Deuflhard and Weber [5] proposed PCCA+ as a new cluster algorithm in conformation dynamics for computational drug design. This method was originally designed for the identification of almost invariant subsets of states in a Markov chain. As an advantage, PCCA+ provides an indicator for the number of clusters. It turned out that PCCA+ can also be applied to other problems in life science. We are going to show how it serves for the clustering of gene expression data stemming from breast cancer research [20]. We also demonstrate that PCCA+ can be used for the clustering of HIV protease inhibitors corresponding to their activity. In theoretical chemistry, PCCA+ is applied to the analysis of metastable ensembles in monomolecular kinetics, which is a tool for RNA folding [21].

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Authors and Affiliations

  1. Zuse Institute Berlin (ZIB), Germany

    Marcus Weber & Susanna Kube

Authors
  1. Marcus Weber
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  2. Susanna Kube
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Editor information

Editors and Affiliations

  1. Berkeley Initiative in Soft Computing (BISC), University of California at Berkeley, USA

    Michael R. Berthold

  2. Unilever Centre for Molecular Informatics, Department of Chemistry, Cambridge University, CB2 1EW, Cambridge, UK

    Robert C. Glen

  3. Department of Biology, University of Konstanz, Box M647, 78457, Konstanz, Germany

    Kay Diederichs

  4. Division for Simulation of Biological Systems, Center for Bioinformatics Tübingen, Eberhard Karls University Tübingen, 72076, Tübingen, Germany

    Oliver Kohlbacher

  5. ALTANA Chair for Bioinformatics and Information Mining, University of Konstanz, Germany

    Ingrid Fischer

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© 2005 Springer-Verlag Berlin Heidelberg

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Weber, M., Kube, S. (2005). Robust Perron Cluster Analysis for Various Applications in Computational Life Science. In: R. Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds) Computational Life Sciences. CompLife 2005. Lecture Notes in Computer Science(), vol 3695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11560500_6

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  • DOI: https://doi.org/10.1007/11560500_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29104-6

  • Online ISBN: 978-3-540-31726-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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