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47Glioblastoma gene expression profile diagnostics by the artificial neural networks. / Mekler, A.A.; Knyazeva, I.; Schwartz, D.R.; Kuperin, Y.A.; Dmitrenko, V.V.; Rymar, V.I.; Kavsan, V.M.

In: Optical Memory and Neural Networks (Information Optics), Vol. 19, No. 2, 2010, p. 181-186.

Research output: Contribution to journal › Article › peer-review

Harvard

Mekler, AA, Knyazeva, I, Schwartz, DR, Kuperin, YA, Dmitrenko, VV, Rymar, VI & Kavsan, VM 2010, '47Glioblastoma gene expression profile diagnostics by the artificial neural networks', Optical Memory and Neural Networks (Information Optics), vol. 19, no. 2, pp. 181-186. https://doi.org/10.3103/S1060992X10020098

APA

Mekler, A. A., Knyazeva, I., Schwartz, D. R., Kuperin, Y. A., Dmitrenko, V. V., Rymar, V. I., & Kavsan, V. M. (2010). 47Glioblastoma gene expression profile diagnostics by the artificial neural networks. Optical Memory and Neural Networks (Information Optics), 19(2), 181-186. https://doi.org/10.3103/S1060992X10020098

Vancouver

Mekler AA, Knyazeva I, Schwartz DR, Kuperin YA, Dmitrenko VV, Rymar VI et al. 47Glioblastoma gene expression profile diagnostics by the artificial neural networks. Optical Memory and Neural Networks (Information Optics). 2010;19(2):181-186. https://doi.org/10.3103/S1060992X10020098

Author

Mekler, A.A. ; Knyazeva, I. ; Schwartz, D.R. ; Kuperin, Y.A. ; Dmitrenko, V.V. ; Rymar, V.I. ; Kavsan, V.M. / 47Glioblastoma gene expression profile diagnostics by the artificial neural networks. In: Optical Memory and Neural Networks (Information Optics). 2010 ; Vol. 19, No. 2. pp. 181-186.

BibTeX

@article{27ecc1f4370a45aa91a821e5a2148113,
title = "47Glioblastoma gene expression profile diagnostics by the artificial neural networks",
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. {\textcopyright} 2010 Allerton Press, Inc.",
author = "A.A. Mekler and I. Knyazeva and D.R. Schwartz and Y.A. Kuperin and V.V. Dmitrenko and V.I. Rymar and V.M. Kavsan",
year = "2010",
doi = "10.3103/S1060992X10020098",
language = "English",
volume = "19",
pages = "181--186",
journal = "Optical Memory and Neural Networks (Information Optics)",
issn = "1060-992X",
publisher = "Springer Nature",
number = "2",

}

RIS

TY - JOUR

T1 - 47Glioblastoma gene expression profile diagnostics by the artificial neural networks

AU - Mekler, A.A.

AU - Knyazeva, I.

AU - Schwartz, D.R.

AU - Kuperin, Y.A.

AU - Dmitrenko, V.V.

AU - Rymar, V.I.

AU - Kavsan, V.M.

PY - 2010

Y1 - 2010

N2 - 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. © 2010 Allerton Press, Inc.

AB - 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. © 2010 Allerton Press, Inc.

U2 - 10.3103/S1060992X10020098

DO - 10.3103/S1060992X10020098

M3 - Article

VL - 19

SP - 181

EP - 186

JO - Optical Memory and Neural Networks (Information Optics)

JF - Optical Memory and Neural Networks (Information Optics)

SN - 1060-992X

IS - 2

ER -

ID: 8183303