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 | United States |
| City | Orlando, FL |
| Period | 7/12/10 → 7/14/10 |
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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
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TY - GEN
T1 - A comparative evaluation of gene set analysis techniques in predictive classification of expression samples
AU - Holec, Matej
AU - Zelezny, Filip
AU - Klema, Jiri
AU - Tolar, Jakub
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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
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
UR - http://www.scopus.com/inward/record.url?scp=84878124944&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878124944&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781617820694
SP - 7
EP - 11
BT - International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics 2010, BCBGC 2010
ER -