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dc.contributor.author
Stegmayer, Georgina
dc.contributor.author
Gerard, Matias Fernando
dc.contributor.author
Milone, Diego Humberto
dc.date.available
2023-03-07T12:14:21Z
dc.date.issued
2012-10
dc.identifier.citation
Stegmayer, Georgina; Gerard, Matias Fernando; Milone, Diego Humberto; Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629); Institute of Electrical and Electronics Engineers; Ieee Computational Intelligence Magazine; 7; 4; 10-2012; 22-34
dc.identifier.issn
1556-603X
dc.identifier.uri
http://hdl.handle.net/11336/189789
dc.description.abstract
Biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations; a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters; a validation measure that can assess the biological significance of the clusters found; and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Data mining
dc.subject
systems biology
dc.subject
clustering
dc.subject
validation
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
Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-03-07T11:25:59Z
dc.journal.volume
7
dc.journal.number
4
dc.journal.pagination
22-34
dc.journal.pais
Estados Unidos
dc.journal.ciudad
New York
dc.description.fil
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Gerard, Matias Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Ieee Computational Intelligence Magazine
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/6331731
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/MCI.2012.2215122
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