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Mendeley readers
Attention Score in Context
Title |
Maximum Parsimony on Phylogenetic networks
|
---|---|
Published in |
Algorithms for Molecular Biology, May 2012
|
DOI | 10.1186/1748-7188-7-9 |
Pubmed ID | |
Authors |
Lavanya Kannan, Ward C Wheeler |
Abstract |
Phylogenetic networks are generalizations of phylogenetic trees, that are used to model evolutionary events in various contexts. Several different methods and criteria have been introduced for reconstructing phylogenetic trees. Maximum Parsimony is a character-based approach that infers a phylogenetic tree by minimizing the total number of evolutionary steps required to explain a given set of data assigned on the leaves. Exact solutions for optimizing parsimony scores on phylogenetic trees have been introduced in the past. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 230 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | <1% |
Sweden | 1 | <1% |
Argentina | 1 | <1% |
Unknown | 227 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 70 | 30% |
Student > Master | 29 | 13% |
Student > Ph. D. Student | 15 | 7% |
Researcher | 13 | 6% |
Student > Doctoral Student | 8 | 3% |
Other | 23 | 10% |
Unknown | 72 | 31% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 51 | 22% |
Biochemistry, Genetics and Molecular Biology | 49 | 21% |
Computer Science | 18 | 8% |
Immunology and Microbiology | 9 | 4% |
Environmental Science | 6 | 3% |
Other | 24 | 10% |
Unknown | 73 | 32% |
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 01 December 2022.
All research outputs
#17,373,204
of 25,497,142 outputs
Outputs from Algorithms for Molecular Biology
#150
of 265 outputs
Outputs of similar age
#115,503
of 175,978 outputs
Outputs of similar age from Algorithms for Molecular Biology
#7
of 9 outputs
Altmetric has tracked 25,497,142 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 265 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 30th percentile – i.e., 30% 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 175,978 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 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.