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Efficient FPT Algorithms for (Strict) Compatibility of Unrooted Phylogenetic Trees

Overview of attention for article published in Bulletin of Mathematical Biology, February 2017
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Article details
Title
Efficient FPT Algorithms for (Strict) Compatibility of Unrooted Phylogenetic Trees
Published in
Bulletin of Mathematical Biology, February 2017
DOI 10.1007/s11538-017-0260-y
Pubmed ID
Authors
Abstract

In phylogenetics, a central problem is to infer the evolutionary relationships between a set of species X; these relationships are often depicted via a phylogenetic tree-a tree having its leaves labeled bijectively by elements of X and without degree-2 nodes-called the "species tree." One common approach for reconstructing a species tree consists in first constructing several phylogenetic trees from primary data (e.g., DNA sequences originating from some species in X), and then constructing a single phylogenetic tree maximizing the "concordance" with the input trees. The obtained tree is our estimation of the species tree and, when the input trees are defined on overlapping-but not identical-sets of labels, is called "supertree." In this paper, we focus on two problems that are central when combining phylogenetic trees into a supertree: the compatibility and the strict compatibility problems for unrooted phylogenetic trees. These problems are strongly related, respectively, to the notions of "containing as a minor" and "containing as a topological minor" in the graph community. Both problems are known to be fixed parameter tractable in the number of input trees k, by using their expressibility in monadic second-order logic and a reduction to graphs of bounded treewidth. Motivated by the fact that the dependency on k of these algorithms is prohibitively large, we give the first explicit dynamic programming algorithms for solving these problems, both running in time [Formula: see text], where n is the total size of the input.

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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 2 40%
Researcher 1 20%
Student > Ph. D. Student 1 20%
Unknown 1 20%
Readers by discipline
Readers by discipline Count As %
Environmental Science 2 40%
Unknown 3 60%
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 19 March 2017.
All research outputs
#27,205,565
of 32,950,213 outputs
Outputs from Bulletin of Mathematical Biology
#1,219
of 1,478 outputs
Outputs of similar age
#271,313
of 342,250 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#18
of 26 outputs
Altmetric has tracked 32,950,213 research outputs across all sources so far. This one is in the 9th percentile – i.e., 9% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,478 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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