↓ Skip to main content

Phylogenetic network analysis as a parsimony optimization problem

Overview of attention for article published in BMC Bioinformatics, September 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
13 tweeters
googleplus
2 Google+ users

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
32 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Phylogenetic network analysis as a parsimony optimization problem
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0675-0
Pubmed ID
Authors

Ward C Wheeler

Abstract

Many problems in comparative biology are, or are thought to be, best expressed as phylogenetic "networks" as opposed to trees. In trees, vertices may have only a single parent (ancestor), while networks allow for multiple parent vertices. There are two main interpretive types of networks, "softwired" and "hardwired." The parsimony cost of hardwired networks is based on all changes over all edges, hence must be greater than or equal to the best tree cost contained ("displayed") by the network. This is in contrast to softwired, where each character follows the lowest parsimony cost tree displayed by the network, resulting in costs which are less than or equal to the best display tree. Neither situation is ideal since hard-wired networks are not generally biologically attractive (since individual heritable characters can have more than one parent) and softwired networks can be trivially optimized (containing the best tree for each character). Furthermore, given the alternate cost scenarios of trees and these two flavors of networks, hypothesis testing among these explanatory scenarios is impossible. A network cost adjustment (penalty) is proposed to allow phylogenetic trees and soft-wired phylogenetic networks to compete equally on a parsimony optimality basis. This cost is demonstrated for several real and simulated datasets. In each case, the favored graph representation (tree or network) matched expectation or simulation scenario. The softwired network cost regime proposed here presents a quantitative criterion for an optimality-based search procedure where trees and networks can participate in hypothesis testing simultaneously.

Twitter Demographics

The data shown below were collected from the profiles of 13 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 6%
Israel 1 3%
Singapore 1 3%
New Zealand 1 3%
Unknown 27 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 28%
Researcher 8 25%
Student > Master 5 16%
Student > Postgraduate 2 6%
Student > Bachelor 2 6%
Other 5 16%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 56%
Biochemistry, Genetics and Molecular Biology 9 28%
Linguistics 1 3%
Mathematics 1 3%
Social Sciences 1 3%
Other 0 0%
Unknown 2 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 09 April 2016.
All research outputs
#1,607,116
of 11,425,721 outputs
Outputs from BMC Bioinformatics
#745
of 4,225 outputs
Outputs of similar age
#43,704
of 241,037 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 128 outputs
Altmetric has tracked 11,425,721 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,225 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 82% 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 241,037 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 128 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.