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47Glioblastoma gene expression profile diagnostics by the artificial neural networks

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An Erratum to this article was published on 01 December 2010

Abstract

Two artificial neural networks of different types were applied to gene expression profiles in glioblastoma, the most aggressive human brain tumor, and in normal brain tissue. The results of gene expression profiles classification are presented. First method, self organizing maps, gave good discrimination of profiles on the trained map. Another ANN, perceptron, showed a good result of classificatio — more then 95% of the test data set were successfully classified. Due to high correlations between some gene expression values one can suppose, that number of genes necessary for successful classification may be reduced.

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Correspondence to A. A. Mekler.

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The article is published in the original.

An erratum to this article can be found at http://dx.doi.org/10.3103/S1060992X10040132

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Mekler, A.A., Knyazeva, I., Schwartz, D.R. et al. 47Glioblastoma gene expression profile diagnostics by the artificial neural networks. Opt. Mem. Neural Networks 19, 181–186 (2010). https://doi.org/10.3103/S1060992X10020098

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  • DOI: https://doi.org/10.3103/S1060992X10020098

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