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Finding Biologically Accurate Clusterings in Hierarchical Tree Decompositions Using the Variation of Information

  • Conference paper
Research in Computational Molecular Biology (RECOMB 2009)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5541))

Abstract

Hierarchical clustering is a popular method for grouping together similar elements based on a distance measure between them. In many cases, annotations for some elements are known beforehand, which can aid the clustering process. We present a novel approach for decomposing a hierarchical clustering into the clusters that optimally match a set of known annotations, as measured by the variation of information metric. Our approach is general and does not require the user to enter the number of clusters desired. We apply it to two biological domains: finding protein complexes within protein interaction networks and identifying species within metagenomic DNA samples. For these two applications, we test the quality of our clusters by using them to predict complex and species membership, respectively. We find that our approach generally outperforms the commonly used heuristic methods.

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Authors and Affiliations

  1. Department of Computer Science, USA

    Saket Navlakha, Niranjan Nagarajan, Mihai Pop & Carl Kingsford

  2. Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, University of Maryland, College Park, USA

    Saket Navlakha, James White, Niranjan Nagarajan, Mihai Pop & Carl Kingsford

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  1. Saket Navlakha
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  4. Mihai Pop
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  1. Computer Science Department, James H. Clark Center, 318 Campus Drive, RM S266, CA 94305-5428,, Stanford, USA

    Serafim Batzoglou

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Navlakha, S., White, J., Nagarajan, N., Pop, M., Kingsford, C. (2009). Finding Biologically Accurate Clusterings in Hierarchical Tree Decompositions Using the Variation of Information. In: Batzoglou, S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science(), vol 5541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02008-7_29

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  • DOI: https://doi.org/10.1007/978-3-642-02008-7_29

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