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Title:
Evolutionary trees from DNA sequences: A maximum likelihood approach | Journal of Molecular Evolution
Description:
The application of maximum likelihood techniques to the estimation of evolutionary trees from nucleic acid sequence data is discussed. A computationally feasible method for finding such maximum likelihood estimates is developed, and a computer program is available. This method has advantages over the traditional parsimony algorithms, which can give misleading results if rates of evolution differ in different lineages. It also allows the testing of hypotheses about the constancy of evolutionary rates by likelihood ratio tests, and gives rough indication of the error of the estimate of the tree.
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google, scholar, article, likelihood, felsenstein, syst, zool, maximum, privacy, cookies, content, data, information, journal, evolution, access, mol, evol, publish, search, evolutionary, trees, log, research, dna, sequences, parsimony, related, discover, langley, download, united, states, rights, springer, optional, personal, parties, policy, find, track, molecular, approach, november, cite, joseph, explore, estimation, nucleic, sequence,
Topics {βοΈ}
month download article/chapter maximum likelihood approach maximum likelihood techniques maximum likelihood estimates traditional parsimony algorithms molecular evolution aims privacy choices/manage cookies likelihood ratio tests dna sequences related topics gupta maximum parsimony full article pdf related subjects european economic area give misleading results statistical decision theory royalty-free licence conditions privacy policy check access instant access accepting optional cookies article felsenstein computationally feasible method systematics association publication united states department journal finder publish main content log united states government november 1981 volumeΒ 17 evolutionary trees phylogenetic classification evolution differ estimation article journal privacy policy article log personal data legal liability books a united states article cite optional cookies manage preferences 6 systematics association journal publish essential cookies data protection cookies skip subscription content similar content
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headline:Evolutionary trees from DNA sequences: A maximum likelihood approach
description:The application of maximum likelihood techniques to the estimation of evolutionary trees from nucleic acid sequence data is discussed. A computationally feasible method for finding such maximum likelihood estimates is developed, and a computer program is available. This method has advantages over the traditional parsimony algorithms, which can give misleading results if rates of evolution differ in different lineages. It also allows the testing of hypotheses about the constancy of evolutionary rates by likelihood ratio tests, and gives rough indication of the error of the estimate of the tree.
datePublished:
dateModified:
pageStart:368
pageEnd:376
sameAs:https://doi.org/10.1007/BF01734359
keywords:
Evolution
Phylogeny
Maximum likelihood
Parsimony
Estimation
DNA sequences
Evolutionary Biology
Microbiology
Plant Sciences
Plant Genetics and Genomics
Animal Genetics and Genomics
Cell Biology
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headline:Evolutionary trees from DNA sequences: A maximum likelihood approach
description:The application of maximum likelihood techniques to the estimation of evolutionary trees from nucleic acid sequence data is discussed. A computationally feasible method for finding such maximum likelihood estimates is developed, and a computer program is available. This method has advantages over the traditional parsimony algorithms, which can give misleading results if rates of evolution differ in different lineages. It also allows the testing of hypotheses about the constancy of evolutionary rates by likelihood ratio tests, and gives rough indication of the error of the estimate of the tree.
datePublished:
dateModified:
pageStart:368
pageEnd:376
sameAs:https://doi.org/10.1007/BF01734359
keywords:
Evolution
Phylogeny
Maximum likelihood
Parsimony
Estimation
DNA sequences
Evolutionary Biology
Microbiology
Plant Sciences
Plant Genetics and Genomics
Animal Genetics and Genomics
Cell Biology
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