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The Cluster Affinity Distance for Phylogenies

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Bioinformatics Research and Applications (ISBRA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11490))

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Abstract

Studying phylogenetic trees is fundamental to biology and benefitting a vast variety of other research areas. Comparing such trees is essential to such studies for which a growing and diverse collection of tree distances are available. In practice, tree distances suffer from problems that can severely limit their applicability. Notably, these distances include the cluster matching distance that is adapted from the Robinson-Foulds distance to overcome many of the drawbacks of this traditional measure. However, at the same time, the cluster matching distance is much more confined in its application than the Robinson-Foulds distance and makes sacrifices for satisfying the properties of a metric. Here, we propose the cluster affinity distance, a new tree distance that is adapted from the cluster matching distance but has not its drawbacks. Nevertheless, as we show, the cluster affinity distance preserves all of the properties that make the matching distance appealing.

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Acknowledgements

OE is supported by the National Science Foundation under Grant No. 1617626.

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Authors and Affiliations

  1. Department of Computer Engineering and Computer Science, California State University Long Beach, Long Beach, CA, 90840, USA

    Jucheol Moon

  2. Department of Computer Science, Iowa State University, Ames, IA, 50011, USA

    Oliver Eulenstein

Authors
  1. Jucheol Moon
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  2. Oliver Eulenstein
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Correspondence to Oliver Eulenstein .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  2. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  3. Central South University, Changsha, China

    Min Li

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Moon, J., Eulenstein, O. (2019). The Cluster Affinity Distance for Phylogenies. In: Cai, Z., Skums, P., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2019. Lecture Notes in Computer Science(), vol 11490. Springer, Cham. https://doi.org/10.1007/978-3-030-20242-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-20242-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20241-5

  • Online ISBN: 978-3-030-20242-2

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