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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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