Conditional LZ Complexity of DNA Sequences Analysis and its Application in Phylogenetic Tree Reconstruction

2008 ◽  
Author(s):  
Jingjun Liu ◽  
Dachao Li
Keyword(s):  
Phylogenetic Tree ◽  
Dna Sequences ◽  
Tree Reconstruction ◽  
Phylogenetic Tree Reconstruction ◽  
Sequences Analysis ◽  
Lz Complexity

scholarly journals Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks

2020 ◽  
Vol 37 (12) ◽  
pp. 3632-3641
Author(s):  
Alina F Leuchtenberger ◽  
Stephen M Crotty ◽  
Tamara Drucks ◽  
Heiko A Schmidt ◽  
Sebastian Burgstaller-Muehlbacher ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Maximum Likelihood ◽  
Phylogenetic Tree ◽  
Maximum Parsimony ◽  
Reconstruction Method ◽  
Long Branch Attraction ◽  
Tree Reconstruction ◽  
Phylogenetic Tree Reconstruction ◽  
The Neural Network

Abstract Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.

On the Repeat-Annotated Phylogenetic Tree Reconstruction Problem

2006 ◽  
Vol 13 (8) ◽  
pp. 1397-1418 ◽  
Author(s):  
Firas Swidan ◽  
Michal Ziv-Ukelson ◽  
Ron Y. Pinter
Keyword(s):  
Phylogenetic Tree ◽  
Reconstruction Problem ◽  
Tree Reconstruction ◽  
Phylogenetic Tree Reconstruction
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