Detection of gene expressions in microarrays by applying iteratively elastic neural net

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datacite.alternateIdentifier.citationADAPTIVE AND NATURAL COMPUTING ALGORITHMS, PT 2,Vol.4432,355-+,2007
datacite.creatorChacon, Max
datacite.creatorLévano Huamaccto, Marcos
datacite.creatorAllende, Hector
datacite.creatorNowak, Hans
datacite.creatorBeliczynski, B
datacite.creatorDz ielinski, A
datacite.creatorIwanowski, M
datacite.creatorRibeiro, B
datacite.date2007
datacite.titleDetection of gene expressions in microarrays by applying iteratively elastic neural net
dc.date.accessioned2021-04-30T16:46:59Z
dc.date.available2021-04-30T16:46:59Z
dc.description.abstractDNA analysis by microarrays is a powerful tool that allows replication of the RNA of hundreds of thousands of genes at the same time, generating a large amount of data in multidimensional space that must be analyzed using informatics tools. Various clustering techniques have been applied to analyze the microarrays, but they do not offer a systematic form of analysis. This paper proposes the use of Gorban's Elastic Neural Net in an iterative way to find patterns of expressed genes. The new method proposed (Iterative Elastic Neural Net, IENN) has been evaluated with up-regulated genes of the Escherichia Coli bacterium and is compared with the Self-Organizing Maps (SOM) technique frequently used in this kind of analysis. The results show that the proposed method finds 86.7% of the up-regulated genes, compared to 65.2% of genes found by the SOM. A comparative analysis of Receiver Operating Characteristic (ROC) with SOM shows that the proposed method is 11.5 % more effective.
dc.identifier.urihttp://repositoriodigital.uct.cl/handle/10925/3470
dc.language.isoen
dc.publisherSPRINGER-VERLAG BERLIN
dc.sourceADAPTIVE AND NATURAL COMPUTING ALGORITHMS, PT 2
oaire.resourceTypeMeeting
uct.catalogadorWOS
uct.indizacionISTP
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