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dc.contributor.author Milone, Diego Humberto
dc.contributor.author Stegmayer, Georgina
dc.contributor.author Kamenetzky, Laura
dc.contributor.author Lopez, Mariana Gabriela
dc.contributor.author Carrari, Fernando Oscar
dc.date.available 2017-06-26T18:00:22Z
dc.date.issued 2013-07
dc.identifier.citation Milone, Diego Humberto; Stegmayer, Georgina; Kamenetzky, Laura; Lopez, Mariana Gabriela; Carrari, Fernando Oscar; Clustering biological data with SOMs: on topology preservation in non-linear dimensional reduction; Elsevier; Expert Systems with Applications; 40; 9; 7-2013; 3841-3845
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/11336/18882
dc.description.abstract Dimensional reduction is a widely used technique for exploratory analysis of large volume of data. In biological datasets, each object is described by a large number of variables (or dimensions) and it is crucial to perform their analyses in a smaller space, to extract useful information. Kohonen self-organizing maps (SOMs) have been recently proposed in systems biology as a useful tool for exploratory analysis, data integration and discovery of new relationships in *omics datasets. SOMs have been traditionally used for clustering in several data mining problems, mainly due to their ability to preserve input data topology and reduce a high dimensional input space into a 2-D map. In spite of this, the above-mentioned dimensional reduction can lead to counterintuitive results. Sometimes, maps having almost the same size, trained on the same dataset, and with identical learning algorithms and parameters, may find different clusters. However, one would expect that small changes in map sizes or another training condition would not result in an abrupt different location of any of the grouped patterns. The aim of this work is to analyze and explain this issue through a real case study involving transcriptomic and metabolomic data, since it might have an important impact when interpreting clustering results over a biological dataset.
dc.format application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject clustering
dc.subject bioinformatics
dc.subject dimensional reduction
dc.subject topology preservation
dc.subject.classification Ciencias de la Información y Bioinformática
dc.subject.classification Ciencias de la Computación e Información
dc.subject.classification CIENCIAS NATURALES Y EXACTAS
dc.title Clustering biological data with SOMs: on topology preservation in non-linear dimensional reduction
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2017-06-08T19:47:04Z
dc.journal.volume 40
dc.journal.number 9
dc.journal.pagination 3841-3845
dc.journal.pais Países Bajos
dc.journal.ciudad Amsterdam
dc.description.fil Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
dc.description.fil Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
dc.description.fil Fil: Kamenetzky, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina
dc.description.fil Fil: Lopez, Mariana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina
dc.description.fil Fil: Carrari, Fernando Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina
dc.journal.title Expert Systems with Applications
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.eswa.2012.12.074
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417412013152?via%3Dihub


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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)