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The combinatorics of discrete time-trees: theory and open problems

Overview of attention for article published in Journal of Mathematical Biology, July 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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12 Mendeley
Title
The combinatorics of discrete time-trees: theory and open problems
Published in
Journal of Mathematical Biology, July 2017
DOI 10.1007/s00285-017-1167-9
Pubmed ID
Authors

Alex Gavryushkin, Chris Whidden, Frederick A. Matsen

Abstract

A time-tree is a rooted phylogenetic tree such that all internal nodes are equipped with absolute divergence dates and all leaf nodes are equipped with sampling dates. Such time-trees have become a central object of study in phylogenetics but little is known about the parameter space of such objects. Here we introduce and study a hierarchy of discrete approximations of the space of time-trees from the graph-theoretic and algorithmic point of view. One of the basic and widely used phylogenetic graphs, the [Formula: see text] graph, is the roughest approximation and bottom level of our hierarchy. More refined approximations discretize the relative timing of evolutionary divergence and sampling dates. We study basic graph-theoretic questions for these graphs, including the size of neighborhoods, diameter upper and lower bounds, and the problem of finding shortest paths. We settle many of these questions by extending the concept of graph grammars introduced by Sleator, Tarjan, and Thurston to our graphs. Although time values greatly increase the number of possible trees, we show that 1-neighborhood sizes remain linear, allowing for efficient local exploration and construction of these graphs. We also obtain upper bounds on the r-neighborhood sizes of these graphs, including a smaller bound than was previously known for [Formula: see text]. Our results open up a number of possible directions for theoretical investigation of graph-theoretic and algorithmic properties of the time-tree graphs. We discuss the directions that are most valuable for phylogenetic applications and give a list of prominent open problems for those applications. In particular, we conjecture that the split theorem applies to shortest paths in time-tree graphs, a property not shared in the general [Formula: see text] case.

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The data shown below were collected from the profiles of 6 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 42%
Researcher 4 33%
Student > Bachelor 1 8%
Student > Master 1 8%
Unknown 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 25%
Mathematics 2 17%
Biochemistry, Genetics and Molecular Biology 2 17%
Computer Science 2 17%
Social Sciences 1 8%
Other 0 0%
Unknown 2 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 11 February 2019.
All research outputs
#12,855,965
of 22,996,001 outputs
Outputs from Journal of Mathematical Biology
#240
of 662 outputs
Outputs of similar age
#146,142
of 316,703 outputs
Outputs of similar age from Journal of Mathematical Biology
#3
of 15 outputs
Altmetric has tracked 22,996,001 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 662 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 63% 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 316,703 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.