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A Linear-Time Majority Tree Algorithm

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Algorithms in Bioinformatics (WABI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 2812))

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Abstract

We give a randomized linear-time algorithm for computing the majority rule consensus tree. The majority rule tree is widely used for summarizing a set of phylogenetic trees, which is usually a post-processing step in constructing a phylogeny. We are implementing the algorithm as part of an interactive visualization system for exploring distributions of trees, where speed is a serious concern for real-time interaction. The linear running time is achieved by using succinct representation of the subtrees and efficient methods for the final tree reconstruction.

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References

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Author information

Authors and Affiliations

  1. Computer Science Department, University of California, 2063 Engineering II, One Sheilds Ave, Davis, CA, 95616, USA

    Nina Amenta

  2. Dept. of Mathematics & Computer Science, Lehman College– City University of New York, Bronx, NY, 12581, USA

    Frederick Clarke & Katherine St. John

  3. Department of Computer Science, CUNY Graduate Center, New York, NY, 10016, USA

    Katherine St. John

Authors
  1. Nina Amenta
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  2. Frederick Clarke
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  3. Katherine St. John
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Editor information

Editors and Affiliations

  1. Department of Biomathematical Sciences, The Mount Sinai School of Medicine, 10029-6574, New York, NY

    Gary Benson

  2. Institute of Biomedical and Life Sciences, Division of Environmental and Evolutionary Biology, University of Glasgow, G12 8QQ, Glasgow, Scotland

    Roderic D. M. Page

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© 2003 Springer-Verlag Berlin Heidelberg

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Amenta, N., Clarke, F., St. John, K. (2003). A Linear-Time Majority Tree Algorithm. In: Benson, G., Page, R.D.M. (eds) Algorithms in Bioinformatics. WABI 2003. Lecture Notes in Computer Science(), vol 2812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39763-2_17

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  • DOI: https://doi.org/10.1007/978-3-540-39763-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20076-5

  • Online ISBN: 978-3-540-39763-2

  • eBook Packages: Springer Book Archive

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