Abstract
A recent research article, entitled Taxon ordering in phylogenetic trees: a workbench test presented the application of an evolutionary algorithm to order taxa in a phylogenetic tree, according to a given distance matrix. In previous articles, the authors introduced the first approaches to study the influence of algorithm parameters on the efficacy of finding the tree with the shortest distance among taxa, based on genetic distances. In the considered work, the authors tested the algorithm using both genetic and geographic distances, and a combination of the two, on three phylogenetic trees of different viruses. The results were interesting, especially when applying geographic distances, allowing a new reading direction, orthogonal to the classical root-to-taxa one.
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References
Moscato, P., Buriol, L., Cotta, C.: On the analysis of data derived from mitochondrial DNA distance matrices: Kolmogorov and a traveling salesman give their opinion. In: Advances in Nature Inspired Computation: The PPSN VII Workshops 2002, pp. 37–38 (2002)
Cotta, C., Moscato, P.: A memetic-aided approach to hierarchical clustering from distance matrices: application to gene expression clustering and phylogeny. Biosystems 72, 75–97 (2003)
Cerutti, F., Bertolotti, L., Goldberg, T.L., Giacobini, M.: Taxon ordering in phylogenetic trees: a workbench test. BMC Bioinformatics 12, 58 (2011)
Cerutti, F., Bertolotti, L., Goldberg, T.L., Giacobini, M.: Taxon ordering in phylogenetic trees by means of evolutionary algorithms. BioData Mining 4, 20 (2010)
Perez, A.M., Pauszek, S.J., Jimenez, D., Kelley, W.N., Whedbee, Z., Rodriguez, L.L.: Spatial and phylogenetic analysis of vesicular stomatitis virus over- wintering in the United States. Preventive Veterinary Medicine 93(4), 258–264 (2010)
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Cerutti, F., Bertolotti, L., Goldberg, T.L., Giacobini, M. (2012). Improving Phylogenetic Tree Interpretability by Means of Evolutionary Algorithms. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2012. Lecture Notes in Computer Science, vol 7246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29066-4_23
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DOI: https://doi.org/10.1007/978-3-642-29066-4_23
Publisher Name: Springer, Berlin, Heidelberg
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