title: 47glioblastoma Gene Expression Profile Diagnostics by the Artificial Neural Networks1
metadata.name: dc.contributor.author
metadata.value: Kuperin, Y. A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mekler, A. A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kniazeva, I.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Schwartz, D. R.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Dmitrenko, V. V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Rimar, V. I.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kavsan, V. M.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2015-08-27T12:43:26Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2015-08-27T12:43:26Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2010
metadata.lang: -
metadata.name: dc.identifier.issn
metadata.value: 1060-992X
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/1686
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: 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.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: Allerton Press, Inc.
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Optical Memory and Neural Networks (Information Optics);Vol. 19, No. 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: Glioblastoma
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: self-organizing maps
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: 47glioblastoma Gene Expression Profile Diagnostics by the Artificial Neural Networks1
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: OptMem&NN_2010_Kuperin.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/1686/1/OptMem%26NN_2010_Kuperin.pdf
resources.format: Adobe PDF
resources.size: 918,12 kB