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SUNPLIN: Simulation with Uncertainty for Phylogenetic Investigations

Overview of attention for article published in BMC Bioinformatics, November 2013
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Mentioned by

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2 tweeters

Citations

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17 Dimensions

Readers on

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70 Mendeley
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Title
SUNPLIN: Simulation with Uncertainty for Phylogenetic Investigations
Published in
BMC Bioinformatics, November 2013
DOI 10.1186/1471-2105-14-324
Pubmed ID
Authors

Wellington S Martins, Welton C Carmo, Humberto J Longo, Thierson C Rosa, Thiago F Rangel

Abstract

Phylogenetic comparative analyses usually rely on a single consensus phylogenetic tree in order to study evolutionary processes. However, most phylogenetic trees are incomplete with regard to species sampling, which may critically compromise analyses. Some approaches have been proposed to integrate non-molecular phylogenetic information into incomplete molecular phylogenies. An expanded tree approach consists of adding missing species to random locations within their clade. The information contained in the topology of the resulting expanded trees can be captured by the pairwise phylogenetic distance between species and stored in a matrix for further statistical analysis. Thus, the random expansion and processing of multiple phylogenetic trees can be used to estimate the phylogenetic uncertainty through a simulation procedure. Because of the computational burden required, unless this procedure is efficiently implemented, the analyses are of limited applicability.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
United States 1 1%
Unknown 67 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 24%
Researcher 12 17%
Student > Master 10 14%
Student > Bachelor 8 11%
Student > Doctoral Student 4 6%
Other 14 20%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 51%
Environmental Science 12 17%
Biochemistry, Genetics and Molecular Biology 4 6%
Computer Science 4 6%
Medicine and Dentistry 3 4%
Other 5 7%
Unknown 6 9%

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
#11,070,874
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#4,250
of 5,420 outputs
Outputs of similar age
#120,190
of 184,606 outputs
Outputs of similar age from BMC Bioinformatics
#188
of 231 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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 184,606 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 231 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.