Phylogenetic Inference
By Christian M. Zmasek. Encyclopedia of Parallel Computing, 2011
623 citations recorded by the source corpus.
Subfields: Paleontology
View Paper: https://www.semanticscholar.org/paper/d9b64fc02ab90d0c96cd99bb6365a2b5cdcf89c4
How researchers see this paper
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In Paleontology
- M. Weksler
- L. P. Costa
- A. Percequillo
- Pablo A. Goloboff
- Jan P. Meier-Kolthoff
- Stéphane Guindon
- Xiaofan Zhou
- Olivier Gascuel
Citations in the corpus
- TNT version 1.5, including a full implementation of phylogenetic morphometrics (2016, background): “Evaluating tree-scores or finding optimal ancestral conditions is also a difficult problem in other criteria for phylogenetic evaluation, such as maximum likelihood or unaligned sequences (as discussed by, for example, Swofford et al., 1996; Wheeler, 1996), for which only heuristic algorithms exist.”
- VICTOR: genome-based phylogeny and classification of prokaryotic viruses (2017, background): “When distance matrices deviate from ultrametricity (Swofford et al. 1996), which usually happens if the evolution of the underlying sequence data deviated from a molecular clock (Felsenstein 2004), clusters based on a given distance or similarity threshold might not correspond to monophyletic groups”
- Taxonomic use of DNA G+C content and DNA-DNA hybridization in the genomic age. (2014, background): “The deviations from ultrametricity (Swofford et al., 1996) at the 70 % boundary are apparently negligible in many cases.”
- IS A NEW AND GENERAL THEORY OF MOLECULAR SYSTEMATICS EMERGING? (2009, background): “By contrast, data from restriction enzymes, or DNA or protein sequences, are easily and almost effortlessly amenable to phylogenetic analysis, because they come to the researcher already in the form of a character matrix (Hillis et al. 1993; Swofford et al. 1996).”
- Bayesian estimation of fossil phylogenies and the evolution of early to middle Paleozoic crinoids (Echinodermata) (2017, background): “Interested readers are advised to see summaries in Swofford et al. (1996), Lewis (2001), Felsenstein (2004), and Yang (2014).”
- Evaluating Fast Maximum Likelihood-Based Phylogenetic Programs Using Empirical Phylogenomic Data Sets (2017, background): “…four sub-trees connected by a given internal branch are re-arranged to form two new, alternative topologies (Robinson 1971), and Subtree-Pruning-and-Regrafting (SPR), in which a given sub-tree is detached from the full tree and re-inserted onto each of the remaining branches (Swofford et al. 1996).”
- A new genus and species of rodent from the Brazilian Atlantic Forest (Rodentia: Cricetidae: Sigmodontinae: Oryzomyini), with comments on oryzomyine biogeography (2011, methodology): “Morphological characters and DNA sequences were subjected to phylogenetic analyses using cladistic parsimony (Farris, 1983; Swofford et al., 1996) and maximum likelihood approaches (Felsenstein, 1981, 2003; Swofford et al.”
- Mutational dynamics and phylogenetic utility of noncoding chloroplast DNA (2009, methodology): “matrix used for phylogeny inference as suggested by a majority of authors (e.g., Swofford et al. 1996; Grundy and Naylor 1999; Castresana 2000).”
- BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments (2010, methodology): “…aligned sequences (e.g. taken from the multiple sequence alignment), one can use a r×r divergence matrix F to represent the relative proportion of each of the possible character state pairs in the pairwise comparison of these two sequences (as schematized for DNA sequences by formula (19) in [11]).”
- How to use MIGRATE or why are Markov chain Monte Carlo programs difficult to use (2009, methodology): “The mutation models are borrowed from phylogenetics (cf. Swofford et al. 1996; Felsenstein 2004) or 'old-fashioned' population genetics (Kimura and Crow 1964; Kimura and Ohta 1978a; Ohta and Kimura 1973).”
- Estimating maximum likelihood phylogenies with PhyML. (2009, background): “Despite this, simulation studies (3, 4) suggest that the heuristics designed for phylogenetic estimation perform well overall.”