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Tree-Based Unrooted Phylogenetic Networks

Overview of attention for article published in Bulletin of Mathematical Biology, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

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13 X users

Citations

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22 Dimensions

Readers on

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19 Mendeley
Title
Tree-Based Unrooted Phylogenetic Networks
Published in
Bulletin of Mathematical Biology, December 2017
DOI 10.1007/s11538-017-0381-3
Pubmed ID
Authors

A. Francis, K. T. Huber, V. Moulton

Abstract

Phylogenetic networks are a generalization of phylogenetic trees that are used to represent non-tree-like evolutionary histories that arise in organisms such as plants and bacteria, or uncertainty in evolutionary histories. An unrooted phylogenetic network on a non-empty, finite set X of taxa, or network, is a connected, simple graph in which every vertex has degree 1 or 3 and whose leaf set is X. It is called a phylogenetic tree if the underlying graph is a tree. In this paper we consider properties of tree-based networks, that is, networks that can be constructed by adding edges into a phylogenetic tree. We show that although they have some properties in common with their rooted analogues which have recently drawn much attention in the literature, they have some striking differences in terms of both their structural and computational properties. We expect that our results could eventually have applications to, for example, detecting horizontal gene transfer or hybridization which are important factors in the evolution of many organisms.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 16%
Student > Doctoral Student 2 11%
Student > Ph. D. Student 2 11%
Student > Bachelor 2 11%
Professor > Associate Professor 2 11%
Other 2 11%
Unknown 6 32%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 21%
Mathematics 3 16%
Computer Science 3 16%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Physics and Astronomy 1 5%
Other 2 11%
Unknown 5 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 March 2018.
All research outputs
#5,995,956
of 24,483,002 outputs
Outputs from Bulletin of Mathematical Biology
#175
of 1,162 outputs
Outputs of similar age
#110,051
of 448,533 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#3
of 27 outputs
Altmetric has tracked 24,483,002 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,162 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 448,533 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.