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

Matej Holec; Filip Zelezny; Jiri Klema; Jakub Tolar

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 proceeding › Conference contribution

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 language	English (US)
Title of host publication	International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics 2010, BCBGC 2010
Pages	7-11
Number of pages	5
State	Published - Dec 1 2010
Event	2010 International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010 - Orlando, FL, United States Duration: Jul 12 2010 → Jul 14 2010

Other

Other	2010 International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010
Country/Territory	United States
City	Orlando, FL
Period	7/12/10 → 7/14/10

Cite this

A comparative evaluation of gene set analysis techniques in predictive classification of expression samples. / Holec, Matej; Zelezny, Filip; Klema, Jiri; Tolar, Jakub.

International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics 2010, BCBGC 2010. 2010. p. 7-11.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Holec, M, Zelezny, F, Klema, J & Tolar, J 2010, A comparative evaluation of gene set analysis techniques in predictive classification of expression samples. in International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics 2010, BCBGC 2010. pp. 7-11, 2010 International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010, Orlando, FL, United States, 7/12/10.

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AB - 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

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Y2 - 12 July 2010 through 14 July 2010

ER -

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

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