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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DTL reconciliation repair
|
|---|---|
| Published in |
BMC Bioinformatics, March 2017
|
| DOI | 10.1186/s12859-017-1463-9 |
| Pubmed ID | |
| Authors |
Weiyun Ma, Dmitriy Smirnov, Ran Libeskind-Hadas |
| Abstract |
Maximum parsimony phylogenetic tree reconciliation is an important technique for reconstructing the evolutionary histories of hosts and parasites, genes and species, and other interdependent pairs. Since the problem of finding temporally feasible maximum parsimony reconciliations is NP-complete, current methods use either exact algorithms with exponential worst-case running time or heuristics that do not guarantee optimal solutions. We offer an efficient new approach that begins with a potentially infeasible maximum parsimony reconciliation and iteratively "repairs" it until it becomes temporally feasible. In a non-trivial number of cases, this approach finds solutions that are better than those found by the widely-used Jane heuristic. |
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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 25% |
| Student > Ph. D. Student | 2 | 17% |
| Professor | 1 | 8% |
| Librarian | 1 | 8% |
| Student > Master | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 3 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Immunology and Microbiology | 2 | 17% |
| Computer Science | 2 | 17% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Mathematics | 1 | 8% |
| Agricultural and Biological Sciences | 1 | 8% |
| Other | 2 | 17% |
| Unknown | 3 | 25% |
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 02 April 2017.
All research outputs
#20,412,387
of 22,962,258 outputs
Outputs from BMC Bioinformatics
#6,881
of 7,306 outputs
Outputs of similar age
#268,642
of 307,953 outputs
Outputs of similar age from BMC Bioinformatics
#108
of 124 outputs
Altmetric has tracked 22,962,258 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 7,306 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% 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 307,953 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 124 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.