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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SIESTA: enhancing searches for optimal supertrees and species trees
|
|---|---|
| Published in |
BMC Genomics, May 2018
|
| DOI | 10.1186/s12864-018-4621-1 |
| Pubmed ID | |
| Authors |
Pranjal Vachaspati, Tandy Warnow |
| Abstract |
Many supertree estimation and multi-locus species tree estimation methods compute trees by combining trees on subsets of the species set based on some NP-hard optimization criterion. A recent approach to computing large trees has been to constrain the search space by defining a set of "allowed bipartitions", and then use dynamic programming to find provably optimal solutions in polynomial time. Several phylogenomic estimation methods, such as ASTRAL, the MDC algorithm in PhyloNet, FastRFS, and ALE, use this approach. We present SIESTA, a method that can be combined with these dynamic programming algorithms to return a data structure that compactly represents all the optimal trees in the search space. As a result, SIESTA provides multiple capabilities, including: (1) counting the number of optimal trees, (2) calculating consensus trees, (3) generating a random optimal tree, and (4) annotating branches in a given optimal tree by the proportion of optimal trees it appears in. SIESTA improves the accuracy of FastRFS and ASTRAL, and is a general technique for enhancing dynamic programming methods for constrained optimization. |
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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 2 | 18% |
| Other | 1 | 9% |
| Student > Ph. D. Student | 1 | 9% |
| Researcher | 1 | 9% |
| Professor > Associate Professor | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 5 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 18% |
| Chemical Engineering | 1 | 9% |
| Agricultural and Biological Sciences | 1 | 9% |
| Computer Science | 1 | 9% |
| Engineering | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 5 | 45% |
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 13 May 2018.
All research outputs
#18,606,163
of 23,047,237 outputs
Outputs from BMC Genomics
#8,228
of 10,697 outputs
Outputs of similar age
#253,933
of 327,709 outputs
Outputs of similar age from BMC Genomics
#184
of 250 outputs
Altmetric has tracked 23,047,237 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,697 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 250 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.