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An efficient algorithm for testing the compatibility of phylogenies with nested taxa

Overview of attention for article published in Algorithms for Molecular Biology, March 2017
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Title
An efficient algorithm for testing the compatibility of phylogenies with nested taxa
Published in
Algorithms for Molecular Biology, March 2017
DOI 10.1186/s13015-017-0099-7
Pubmed ID
Authors

Deng, Yun, Fernández-Baca, David, Yun Deng, David Fernández-Baca

Abstract

Semi-labeled trees generalize ordinary phylogenetic trees, allowing internal nodes to be labeled by higher-order taxa. Taxonomies are examples of semi-labeled trees. Suppose we are given collection [Formula: see text] of semi-labeled trees over various subsets of a set of taxa. The ancestral compatibility problem asks whether there is a semi-labeled tree that respects the clusterings and the ancestor/descendant relationships implied by the trees in [Formula: see text]. The running time and space usage of the best previous algorithm for testing ancestral compatibility depend on the degrees of the nodes in the trees in [Formula: see text]. We give a algorithm for the ancestral compatibility problem that runs in [Formula: see text] time and uses [Formula: see text] space, where [Formula: see text] is the total number of nodes and edges in the trees in [Formula: see text]. Taxonomies enable researchers to expand greatly the taxonomic coverage of their phylogenetic analyses. The running time of our method does not depend on the degrees of the nodes in the trees in [Formula: see text]. This characteristic is important when taxonomies-which can have nodes of high degree-are used.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 25%
Unknown 3 75%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 25%
Professor 1 25%
Student > Ph. D. Student 1 25%
Researcher 1 25%
Readers by discipline Count As %
Computer Science 3 75%
Agricultural and Biological Sciences 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 10 April 2017.
All research outputs
#4,896,317
of 9,665,907 outputs
Outputs from Algorithms for Molecular Biology
#78
of 173 outputs
Outputs of similar age
#126,881
of 260,601 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 6 outputs
Altmetric has tracked 9,665,907 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 173 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 51% 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 260,601 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.