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(PDF) A Multi-Neighbor-Joining Approach for Phylogenetic ...
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2024年12月5日 — A Multi-Neighbor-Joining Approach for Phylogenetic Tree Reconstruction and Visualization. January 2004. Source; DBLP. Conference: III Brazilian ...
A multi-neighbor-joining approach for phylogenetic tree ...
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由 AEA da Silva 著作2005被引用 15 次 — When there are multiple unrooted tree topologies to be compared, a visualization tool is also proposed, using a radial layout to uniformly ...
A multi-neighbor-joining approach for phylogenetic tree ...
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2024年10月22日 — Evolutionary Biology · Phylogenetic Tree. Article. A multi-neighbor-joining approach for phylogenetic tree reconstruction and visualization.
(PDF) A multi-neighbor-joining approach for phylogenetic tree ...
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Using simulated data, we compared five methods of phylogenetic tree estimation: parsimony, compatibility, maximum likelihood, Fitch-Margoliash, and neighbor ...
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We have developed a phylogenetic tree reconstruction method that detects and reports multiple topologically distant low-cost solutions. Our method is a ...
A Multi-Neighbor-Joining Approach for Phylogenetic Tree ...
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A Multi-Neighbor-Joining Approach for Phylogenetic Tree Reconstruction and Visualization. Work. HTML. Year: 2004. Type: article. Authors Ana Estela Antunes da ...
A Multi-Neighbor-Joining Approach for Phylogenetic Tree ...
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ABSTRACT. The computationally challenging problem of reconstructing the phylogeny of a set of contemporary data, such as DNA sequences or morphological ...
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The MultiFurcating Neighbor-Joining Algorithm for ...
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由 A Fernández 著作2023被引用 9 次 — The MFNJ algorithm produces polytomic phylogenetic trees. The main advantage of using the MFNJ algorithm is that only one phylogenetic tree can be obtained.
a new method for reconstructing phylogenetic trees.
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The neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods for reconstructing phylogenetic trees ...
Wilfredo J. Puma-Villanueva
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A multi-neighbor-joining approach for phylogenetic tree reconstruction and visualization. AE Silva, WJ Villanueva, H Knidel, VC Bonato, SF Reis, FJ Von Zuben.
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