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Reconstructing One-Articulated Networks with Distance Matrices

Overview of attention for article published in Journal of Computational Biology, October 2017
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Article details
Title
Reconstructing One-Articulated Networks with Distance Matrices
Published in
Journal of Computational Biology, October 2017
DOI 10.1089/cmb.2017.0148
Pubmed ID
Authors
Abstract

Given a distance matrix M that represents evolutionary distances between any two species, an edge-weighted phylogenetic network N is said to satisfy M if between any pair of species, there exists a path in N with a length equal to the corresponding entry in M. In this article, we consider a special class of networks called a one-articulated network, which is a proper superset of galled trees. We show that if the distance matrix M is derived from an ultrametric one-articulated network N (i.e., for any species X and Y, the entry [Formula: see text] is equal to the shortest distance between X and Y in N), we can re-construct a network that satisfies M in [Formula: see text] time, where n denotes the number of species; further, the reconstructed network is guaranteed to be the simplest, in a sense that the number of hybrid nodes is minimized. In addition, one may easily index a one-articulated network N with a minimum number of hybrid nodes in [Formula: see text] space, such that on any given phylogenetic tree T, we can determine whether T is contained in N (i.e., if a spanning subtree [Formula: see text] of N is a subdivision of T) in [Formula: see text] time.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Unknown 1 50%
Readers by discipline
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 1 50%
Unknown 1 50%
Attention Score in Context

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 14 October 2017.
All research outputs
#23,629,682
of 29,684,055 outputs
Outputs from Journal of Computational Biology
#712
of 1,158 outputs
Outputs of similar age
#256,782
of 344,721 outputs
Outputs of similar age from Journal of Computational Biology
#10
of 29 outputs
Altmetric has tracked 29,684,055 research outputs across all sources so far. This one is in the 17th percentile – i.e., 17% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,158 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 31st percentile – i.e., 31% 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 344,721 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.