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Attention Score in Context
| Title |
A program to compute the soft Robinson–Foulds distance between phylogenetic networks
|
|---|---|
| Published in |
BMC Genomics, March 2017
|
| DOI | 10.1186/s12864-017-3500-5 |
| Pubmed ID | |
| Authors |
Bingxin Lu, Louxin Zhang, Hon Wai Leong |
| Abstract |
Over the past two decades, phylogenetic networks have been studied to model reticulate evolutionary events. The relationships among phylogenetic networks, phylogenetic trees and clusters serve as the basis for reconstruction and comparison of phylogenetic networks. To understand these relationships, two problems are raised: the tree containment problem, which asks whether a phylogenetic tree is displayed in a phylogenetic network, and the cluster containment problem, which asks whether a cluster is represented at a node in a phylogenetic network. Both the problems are NP-complete. A fast exponential-time algorithm for the cluster containment problem on arbitrary networks is developed and implemented in C. The resulting program is further extended into a computer program for fast computation of the Soft Robinson-Foulds distance between phylogenetic networks. Two computer programs are developed for facilitating reconstruction and validation of phylogenetic network models in evolutionary and comparative genomics. Our simulation tests indicated that they are fast enough for use in practice. Additionally, the distribution of the Soft Robinson-Foulds distance between phylogenetic networks is demonstrated to be unlikely normal by our simulation data. |
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 % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 43% |
| Researcher | 1 | 14% |
| Student > Doctoral Student | 1 | 14% |
| Unknown | 2 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 29% |
| Veterinary Science and Veterinary Medicine | 1 | 14% |
| Agricultural and Biological Sciences | 1 | 14% |
| Medicine and Dentistry | 1 | 14% |
| Unknown | 2 | 29% |
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 20 October 2017.
All research outputs
#17,885,520
of 22,962,258 outputs
Outputs from BMC Genomics
#7,603
of 10,686 outputs
Outputs of similar age
#221,107
of 307,953 outputs
Outputs of similar age from BMC Genomics
#133
of 200 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,686 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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We're also able to compare this research output to 200 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.