Cargando…
Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks
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-tax...
Autores principales: | Leuchtenberger, Alina F, Crotty, Stephen M, Drucks, Tamara, Schmidt, Heiko A, Burgstaller-Muehlbacher, Sebastian, von Haeseler, Arndt |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743852/ https://www.ncbi.nlm.nih.gov/pubmed/32637998 http://dx.doi.org/10.1093/molbev/msaa164 |
Ejemplares similares
-
Felsenstein Phylogenetic Likelihood
por: Posada, David, et al.
Publicado: (2021) -
IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies
por: Nguyen, Lam-Tung, et al.
Publicado: (2015) -
Renewing Felsenstein’s Phylogenetic Bootstrap in the Era of
Big Data
por: Lemoine, F., et al.
Publicado: (2018) -
NGC: lossless and lossy compression of aligned high-throughput sequencing data
por: Popitsch, Niko, et al.
Publicado: (2013) -
Benefit-of-doubt (BOD) scoring: A sequencing-based method for SNP candidate assessment from high to medium read number data sets
por: Sedlazeck, Fritz Joachim, et al.
Publicado: (2013)