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) |
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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 |
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Country | United States |
City | Orlando, FL |
Period | 7/12/10 → 7/14/10 |