Powiadomienia systemowe
- Sesja wygasła!
Tytuł artykułu
Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Dudoit et al., Lee et al., and Statnikov et al. investigated the performance of several classification methods applied to real-life microarray data. Due to the availability of only a few datasets, only a limited number of settings could be evaluated. Also, the true classification and the set of truly differentially expressed genes were unknown. In order to overcome these limitations, a simulation study was conducted, by using a linear mixed effects model to simulate microarray data under different scenarios. Several classification methods were compared with respect to their ability to discriminate between two classes of biological samples in various experimental settings.
Wydawca
Czasopismo
Rocznik
Tom
Strony
15--28
Opis fizyczny
Bibliogr. 12 poz., tab., wykr.
Twórcy
autor
autor
autor
- Hasselt University, Center for Statistics, Agoralaan (building D), B-3590 Diepenbeek, Belgium, suzy.vansanden@uhasselt.be
Bibliografia
- 1. Dudoit S., Fridlyand J., Speed T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 2002, 98, 77-87.
- 2. Lee J.W., Lee J.B., Park M., Song S.H.: Extensive comparison of recent classification tools applied to microarray data. Computational Statistics and Data Analysis 2005,48, 869-885.
- 3. Statnikov A., Aliferis C.F., Tsamardinos I., Hardin D., Levy S.: A comprehensive evaluation of multicateogry classification methods for microarray gene expression cancer diagnosis. Bioinformatics 2005,21,631-643.
- 4. Breiman L., Friedman J.H., Olshen R.A., Stone C.J.: Classification and Regression Trees. Chapman & Hall, New York 1984.
- 5. Breiman L.: Bagging predictors. Machine Learning 1996, 24, 123-140.
- 6. Freund Y., Schapire R.E.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 1999,14, 771-780.
- 7. Breiman L.: Random forests. Machine Learning 2001, 45, 5-32.
- 8. Ripley B. D.: Pattern Recognition and Neural Networks. Cambridge 1996.
- 9. Vapnik N.V.: The nature of Statistical Learning Theory. (2nded.) Springer, New York 2000.
- 10. Van Breda S., van Agen E., van Sanden S., Burzykowski T., Kleinjans J., van Delft J.: Vegetables affect the expression of genes involved in anticarcinogenic processes in the colonic mucosa of C57BL/6 female mice. Journal of Nutrition 2005,135(8), 1879-1888.
- 11. Kerr K.M., Martin M., Churchill G.A.: Analysis of variance for gene expression microarray data. Journal of Computational Biology 2000, 7, 819-838.
- 12. Wolfinger R.D., Gibson G., Wolfinger E.D. et al.: Assessing gene significance from cDNA microarray expression data via mixed models. Journal of Computational Biology 2001, 8, 625-637.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0043-0034