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
Y1 - 2010
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
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M3 - Conference contribution
AN - SCOPUS:84878124944
SN - 9781617820694
T3 - International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics 2010, BCBGC 2010
SP - 7
EP - 11
BT - International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics 2010, BCBGC 2010
T2 - 2010 International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010
Y2 - 12 July 2010 through 14 July 2010
ER -