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Recursive algorithms for phylogenetic tree counting

Overview of attention for article published in Algorithms for Molecular Biology, October 2013
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1 tweeter

Citations

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Title
Recursive algorithms for phylogenetic tree counting
Published in
Algorithms for Molecular Biology, October 2013
DOI 10.1186/1748-7188-8-26
Pubmed ID
Authors

Alexandra Gavryushkina, David Welch, Alexei J Drummond

Abstract

In Bayesian phylogenetic inference we are interested in distributions over a space of trees. The number of trees in a tree space is an important characteristic of the space and is useful for specifying prior distributions. When all samples come from the same time point and no prior information available on divergence times, the tree counting problem is easy. However, when fossil evidence is used in the inference to constrain the tree or data are sampled serially, new tree spaces arise and counting the number of trees is more difficult.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
United Kingdom 1 4%
Brazil 1 4%
United States 1 4%
Iran, Islamic Republic of 1 4%
Unknown 18 78%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 30%
Researcher 5 22%
Professor 3 13%
Student > Master 3 13%
Student > Doctoral Student 2 9%
Other 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 39%
Computer Science 5 22%
Mathematics 4 17%
Biochemistry, Genetics and Molecular Biology 4 17%
Earth and Planetary Sciences 1 4%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 18 November 2013.
All research outputs
#11,053,445
of 12,434,754 outputs
Outputs from Algorithms for Molecular Biology
#148
of 182 outputs
Outputs of similar age
#188,880
of 226,073 outputs
Outputs of similar age from Algorithms for Molecular Biology
#8
of 12 outputs
Altmetric has tracked 12,434,754 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 182 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.