A comparative evaluation of gene set analysis techniques in predictive classification of expression samples

Matej Holec, Filip Zelezny, Jiri Klema, Jakub Tolar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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. Copyright

Original languageEnglish (US)
Title of host publicationInternational Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics 2010, BCBGC 2010
Pages7-11
Number of pages5
StatePublished - Dec 1 2010
Event2010 International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010 - Orlando, FL, United States
Duration: Jul 12 2010Jul 14 2010

Other

Other2010 International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010
CountryUnited States
CityOrlando, FL
Period7/12/107/14/10

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