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Description
  • We demonstrate how some recently developed techniques of set level gene expression data analysis may be exploited in the context of predictive classification of gene expression samples for the tasks of attribute selection and extraction. With four benchmark gene expression datasets, we empirically test the influence of these method on the predictive accuracy of constructed classification models in a comparative setting. Our results mainly indicate that gene set selection methods (SAM GS and the global test) can boost the predictive accuracy if used with caution.
  • We demonstrate how some recently developed techniques of set level gene expression data analysis may be exploited in the context of predictive classification of gene expression samples for the tasks of attribute selection and extraction. With four benchmark gene expression datasets, we empirically test the influence of these method on the predictive accuracy of constructed classification models in a comparative setting. Our results mainly indicate that gene set selection methods (SAM GS and the global test) can boost the predictive accuracy if used with caution. (en)
Title
  • A Comparative Evaluation of Gene Set Analysis Techniques in Predictive Classification of Expression Samples
  • A Comparative Evaluation of Gene Set Analysis Techniques in Predictive Classification of Expression Samples (en)
skos:prefLabel
  • A Comparative Evaluation of Gene Set Analysis Techniques in Predictive Classification of Expression Samples
  • A Comparative Evaluation of Gene Set Analysis Techniques in Predictive Classification of Expression Samples (en)
skos:notation
  • RIV/68407700:21230/10:00169638!RIV11-GA0-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA201/09/1665), P(ME 910), Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 244512
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/10:00169638
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • gene expression; machine learning; data analysis; set-level (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [99A849EBB77D]
http://linked.open...v/mistoKonaniAkce
  • Orlando
http://linked.open...i/riv/mistoVydani
  • Orlando
http://linked.open...i/riv/nazevZdroje
  • International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Holec, Matěj
  • Kléma, Jiří
  • Tolar, J.
  • Železný, Filip
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • International Society for Research in Science and Technology (ISRST)
https://schema.org/isbn
  • 978-1-60651-017-9
http://localhost/t...ganizacniJednotka
  • 21230
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