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

dc.date2007
dc.date.accessioned2012-02-25T00:35:51Z
dc.date.available2012-02-25T00:35:51Z
dc.date.issued2012-02-24
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pte.2, 355-363, 2007es
dc.identifier.urihttps://hdl.handle.net/10925/716
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 SelfOrganizing 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.es
dc.formatPDFes
dc.language.isoenes
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)es
dc.subjectBioinformáticaes
dc.subjectADNes
dc.subjectRedes neuronaleses
dc.titleDetection of gene expressions in microarrays by applying iteratively elastic neural netes
dc.typeArtículo de Revistaes
uct.comunidadIngenieríaes
uct.facultadFacultad de Ingenieríaes
uct.carreraIngeniería Civil en Informáticaes
uct.catalogadorFGEes
uct.indizacionISIes


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