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An approximate maximum likelihood approach, applied to phylogenetic trees

Jönsson, Henrik LU and Söderberg, Bo LU (2003) In Journal of Computational Biology 10(5). p.737-749
Abstract
A novel type of approximation scheme to the maximum likelihood (ML) approach is presented and discussed in the context of phylogenetic tree reconstruction from aligned DNA sequences. It is based on a parameterized approximation to the conditional distribution of hidden variables (related, e.g., to the sequences of unobserved branch point ancestors) given the observed data. A modified likelihood, based on the extended data, is then maximized with respect to the parameters of the model as well as to those involved in the approximation. With a suitable form of the approximations the proposed method allows for simpler updating of the parameters, at the cost of an increased parameter count and a slight decrease in performance. The method is... (More)
A novel type of approximation scheme to the maximum likelihood (ML) approach is presented and discussed in the context of phylogenetic tree reconstruction from aligned DNA sequences. It is based on a parameterized approximation to the conditional distribution of hidden variables (related, e.g., to the sequences of unobserved branch point ancestors) given the observed data. A modified likelihood, based on the extended data, is then maximized with respect to the parameters of the model as well as to those involved in the approximation. With a suitable form of the approximations the proposed method allows for simpler updating of the parameters, at the cost of an increased parameter count and a slight decrease in performance. The method is tested on phylogenetic tree reconstruction from artificially generated sequences, and its performance is compared to that of ML, showing that the approach is competitive for reasonably similar sequences. The method is also applied to real DNA sequences from primates, yielding a result consistent with those obtained by other standard algorithms. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
variational, phylogeny, maximum likelihood, mean-field
in
Journal of Computational Biology
volume
10
issue
5
pages
737 - 749
publisher
Mary Ann Liebert, Inc.
external identifiers
  • wos:000186395700006
  • scopus:0242523717
ISSN
1557-8666
language
English
LU publication?
yes
id
af13fe57-0f74-409d-ade6-4881308c46fa (old id 296464)
date added to LUP
2007-08-03 11:43:38
date last changed
2018-05-29 10:44:16
@article{af13fe57-0f74-409d-ade6-4881308c46fa,
  abstract     = {A novel type of approximation scheme to the maximum likelihood (ML) approach is presented and discussed in the context of phylogenetic tree reconstruction from aligned DNA sequences. It is based on a parameterized approximation to the conditional distribution of hidden variables (related, e.g., to the sequences of unobserved branch point ancestors) given the observed data. A modified likelihood, based on the extended data, is then maximized with respect to the parameters of the model as well as to those involved in the approximation. With a suitable form of the approximations the proposed method allows for simpler updating of the parameters, at the cost of an increased parameter count and a slight decrease in performance. The method is tested on phylogenetic tree reconstruction from artificially generated sequences, and its performance is compared to that of ML, showing that the approach is competitive for reasonably similar sequences. The method is also applied to real DNA sequences from primates, yielding a result consistent with those obtained by other standard algorithms.},
  author       = {Jönsson, Henrik and Söderberg, Bo},
  issn         = {1557-8666},
  keyword      = {variational,phylogeny,maximum likelihood,mean-field},
  language     = {eng},
  number       = {5},
  pages        = {737--749},
  publisher    = {Mary Ann Liebert, Inc.},
  series       = {Journal of Computational Biology},
  title        = {An approximate maximum likelihood approach, applied to phylogenetic trees},
  volume       = {10},
  year         = {2003},
}