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http://hdl.handle.net/11701/1686
Полная запись метаданных
Поле DC | Значение | Язык |
---|---|---|
dc.contributor.author | Kuperin, Y. A. | - |
dc.contributor.author | Mekler, A. A. | - |
dc.contributor.author | Kniazeva, I. | - |
dc.contributor.author | Schwartz, D. R. | - |
dc.contributor.author | Dmitrenko, V. V. | - |
dc.contributor.author | Rimar, V. I. | - |
dc.contributor.author | Kavsan, V. M. | - |
dc.date.accessioned | 2015-08-27T12:43:26Z | - |
dc.date.available | 2015-08-27T12:43:26Z | - |
dc.date.issued | 2010 | - |
dc.identifier.issn | 1060-992X | - |
dc.identifier.uri | http://hdl.handle.net/11701/1686 | - |
dc.description.abstract | Two artificial neural networks of different types were applied to profiles of gene expression in most aggressive human brain tumor – glioblastoma – and in normal brain tissue. The results of gene expression profiles classification are presented. First method – self organizing maps – gave good dis crimination of profiles on the trained map. Another ANN – perceptron – showed a good result of classification – 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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Allerton Press, Inc. | en_GB |
dc.relation.ispartofseries | Optical Memory and Neural Networks (Information Optics);Vol. 19, No. 2 | - |
dc.subject | Glioblastoma | en_GB |
dc.subject | self-organizing maps | en_GB |
dc.title | 47glioblastoma Gene Expression Profile Diagnostics by the Artificial Neural Networks1 | en_GB |
dc.type | Article | en_GB |
Располагается в коллекциях: | Articles |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
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OptMem&NN_2010_Kuperin.pdf | 918,12 kB | Adobe PDF | Просмотреть/Открыть |
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