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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Enumerating all maximal frequent subtrees in collections of phylogenetic trees
|
|---|---|
| Published in |
Algorithms for Molecular Biology, June 2014
|
| DOI | 10.1186/1748-7188-9-16 |
| Pubmed ID | |
| Authors |
Akshay Deepak, David Fernández-Baca |
| Abstract |
A common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 10% |
| Unknown | 9 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 30% |
| Student > Ph. D. Student | 2 | 20% |
| Student > Master | 2 | 20% |
| Professor | 1 | 10% |
| Student > Postgraduate | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 1 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 50% |
| Agricultural and Biological Sciences | 1 | 10% |
| Biochemistry, Genetics and Molecular Biology | 1 | 10% |
| Psychology | 1 | 10% |
| Earth and Planetary Sciences | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 1 | 10% |
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 26 July 2014.
All research outputs
#14,197,145
of 22,757,541 outputs
Outputs from Algorithms for Molecular Biology
#111
of 264 outputs
Outputs of similar age
#120,057
of 228,273 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 4 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 52% 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 228,273 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 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.