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Changiz Eslahchi,
Reza Hassanzadeh,
Ehsan Mottaghi,
Mahnaz Habibi,
Hamid Pezeshk and
Mehdi Sadeghi. Constructing circular phylogenetic networks from weighted quartets using simulated annealing. In MBIO, Vol. 235(2):123-127, 2012. Keywords: abstract network, from quartets, heuristic, phylogenetic network, phylogeny, Program SAQ-Net, Program SplitsTree, reconstruction, simulated annealing, software, split network. Note: http://dx.doi.org/10.1016/j.mbs.2011.11.003.
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"In this paper, we present a heuristic algorithm based on the simulated annealing, SAQ-Net, as a method for constructing phylogenetic networks from weighted quartets. Similar to QNet algorithm, SAQ-Net constructs a collection of circular weighted splits of the taxa set. This collection is represented by a split network. In order to show that SAQ-Net performs better than QNet, we apply these algorithm to both the simulated and actual data sets containing salmonella, Bees, Primates and Rubber data sets. Then we draw phylogenetic networks corresponding to outputs of these algorithms using SplitsTree4 and compare the results. We find that SAQ-Net produces a better circular ordering and phylogenetic networks than QNet in most cases. SAQ-Net has been implemented in Matlab and is available for download at http://bioinf.cs.ipm.ac.ir/softwares/saq.net. © 2011 Elsevier Inc."
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Reza Hassanzadeh,
Changiz Eslahchi and
Wing-Kin Sung. Constructing phylogenetic supernetworks based on simulated annealing. In MPE, Vol. 63(3):738-744, 2012. Keywords: abstract network, from unrooted trees, heuristic, phylogenetic network, phylogeny, Program SNSA, reconstruction, simulated annealing, software, split network. Note: http://dx.doi.org/10.1016/j.ympev.2012.02.009.
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Different partial phylogenetic trees can be derived from different sources of evidence and different methods. One important problem is to summarize these partial phylogenetic trees using a supernetwork. We propose a novel simulated annealing based method called SNSA which uses an optimization function to produce a simple network that still retains a great deal of phylogenetic information. We report the performance of this new method on real and simulated datasets. © 2012 Elsevier Inc.
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