Sentence examples for minimum evolution principle from inspiring English sources
In this paper, we introduce an Ant Colony Optimization (ACO) algorithm to estimate phylogenies under the minimum evolution principle.
Any optimal solution (X*, v*) of problem (1) defines a phylogenetic tree satisfying the minimum evolution principle.
We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle.
We introduce here an Ant Colony Optimization algorithm (ACO) for the phylogeny estimation problem under the minimum evolution principle and demonstrate the feasibility of this approach.
Using sequence simulation, we evaluated the use of the Minimum Evolution principle to place aligned fragmented DNA sequences representing metagenomic reads into phylogenetically correct positions in an existing tree of known sequences.
Here, we introduce the Ant Colony Optimization (ACO) algorithm for estimating phylogenies under the minimum evolution principle, and show that ACO has the potential to compete with other widely-used methods.
FastTree 1.0 is based on the "minimum-evolution" principle – it tries to find a topology that minimizes the amount of evolution, or the sum of the branch lengths.
The Minimum Evolution (ME) principle is a commonly used principle to estimate phylogenetic trees of a set Γ of n species (taxa) given an n × n symmetric matrix D = { d ij } of evolutionary distances.
Therefore, we have developed a distance-based approach (called PhyClass) under the Minimum Evolution (ME) principle for classifying metagenomics reads.
Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME) principle (aiming at recovering the phylogeny with shortest length) is one of the most commonly used optimality criteria for estimating phylogenetic trees.
However, an approach using the Minimum Evolution (ME) principle is not yet available for classifying metagenomic reads, which is important because, as we show below, it is possible to develop a method that does not require heuristics for classifying reads when using a matrix of pairwise distances.
