IDA - Intelligent Data Analysis Research Group

BibTeX Entry

  file = {report_GL212-10.pdf},
  category = {ida-publications},
  author = {Mat{\v e}j Holec and Filip {\v Z}elezn{\'y} and Ji{\v r}{\'i} Kl{\'e}ma and Jakub Tolar},
  title = {Towards Set-Level Predictive Classification of Gene Expression Data},
  year = {2010},
  institution = {CTU, Faculty of  Electrical Engineering, Gerstner Laboratory, Prague},
  abstract = {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.},
  vvvs = {0},

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