Abstract
In this paper we describe how the self-organizing map can be applied to the visualization of the evolution and change of Human Immunodeficiency Virus (HIV) quasiespecies, and how this could be converted into a predictive tool. A SOM is trained with a set of nucleic acid sequences from the HIV-1 virus, and the U-Matrix method is applied to discover what natural groups are formed within it. Results show the validity of the method, and allow to discover two groupings within these sets, and what is the evolutionary path taken in them. © Springer-Veriag Berlin Heidelberg 2007.
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CITATION STYLE
Mora, A. M., Merelo, J. J., Briones, C., Morán, F., & Laredo, J. L. J. (2007). Clustering and visualizing HIV quasispecies using kohonen’s self-organizing maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4507 LNCS, pp. 940–947). Springer Verlag. https://doi.org/10.1007/978-3-540-73007-1_113
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