G. Alexe et al. Breast cancer prognosis by combinatorial analysis of gene expression data. Breast Cancer Res, 8(4):R41, 2006.
Affymetrix. Affymetrix Microarray Suite User Guide, Version 5, 2001.
N. Ancona, R. Maglietta, A. D'Addabbo, S. Liuni, and G. Pesole. Regularized least squares cancer classifiers from DNA microarray data. BMC Bioinformatics, 6(Suppl 4):S2, 2005.
Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B (Methodological), 57:289-300, 1995.
J. Bacardit and N. Krasnogor. Fast rule representation for continuous attributes in genetics-based machine learning. In Genet Evol Comput Conf, pages 1421-1422. ACM, 2008.
G.A. Churchill. Fundamentals of experimental design for cDNA microarrays. Nat Genet, 32:490-495, 2002.
Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. E. Dimitriadou, K. Hornik, F. Leisch, D. Meyer, A. Weingessel, and M.F. Leisch. Misc functions of the department of statistics (e1071), TU Wien, 2005. R-Package e1071 version 1.5-19.
B. Efron and R. Tibshirani. On testing the significance of sets of genes. Ann Appl Stat, 1(1):107-129, 2007.
Y. Freund and R.E. Schapire. Experiments with a new boosting algorithm. In Proc Int Conf Mach Learn, pages 148-156. ACM, 1996.
D. Geman et al. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol, 3(19), 2004.
J.J. Goeman et al. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20(1):93-99, 2004.
Z. Guo et al. Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics, 6(1):58, 2005.
E. Glaab, A. Baudot, N. Krasnogor, and A. Valencia. TopoGSA: network topological gene set analysis. Bioinformatics, 26(9):1271-1272, 2010.
E. Glaab, J.M. Garibaldi, and N. Krasnogor. ArrayMining: a modular webapplication for microarray analysis combining ensemble and consensus methods with cross-study normalization. BMC Bioinformatics, 10(1):358, 2009.
Y. Guo, T. Hastie, and R. Tibshirani. Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1):86-100, 2007.
A.W. Hsing et al. Prostate cancer risk and serum levels of insulin and leptin: a population-based study. J Natl Cancer Inst, 93(10):783-789, 2001.
C. J. Huberty. Applied Discriminant Analysis. John Wiley, New York, 1994.
W. Huber, A. von Heydebreck, H. Sultmann, A. Poustka, and M. Vingron. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics, 18(1):96-104, 2002.
T. Jirapech-Umpai and S. Aitken. Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics, 6(1):148, 2005.
B. Lustig and J. Behrens. The Wnt signaling pathway and its role in tumor development. J Cancer Res Clin Oncol, 129(4):199-221, 2003.
J.L. Lustgarten, V. Gopalakrishnan, H. Grover, and S. Visweswaran. Improving Classification Performance with Discretization on Biomedical Datasets. In AMIA Annu Symp Proc, volume 2008, pages 445-449. AMIA, 2008.
W.J. Lin, H.M. Hsueh, and J.J. Chen. Power and sample size estimation in microarray studies. BMC Bioinformatics, 11(1):48, 2010.
O. Obajimi, J.C. Keen, and P.W. Melera. Inhibition of de novo purine synthesis in human prostate cells results in ATP depletion, AMPK activation and induces senescence. The Prostate, 69(11):1206-1221, 2009.
Y.W. Qiang, Y. Endo, J.S. Rubin, and S. Rudikoff. Wnt signaling in B-cell neoplasia. Oncogene, 22(10):1536-1545, 2003.
M.A. Shipp et al. Diffuse large B-cell lymphoma outcome prediction by geneexpression profiling and supervised machine learning. Nat Med, 8(1):68-74, 2002.
D. Singh et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 1(2):203-209, 2002.
A. Subramanian et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci, 102(43):15545-15550, 2005.
P. Stattin and R. Kaaks. Prostate cancer, insulin, and androgen deprivation therapy. Br J Cancer, 89(9):1814-1815, 2003.
RDevelopment Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2010.
A.C. Tan, D.Q. Naiman, L. Xu, R.L. Winslow, and D. Geman. Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics, 21(20):3896-3904, 2005.
J. Van Hulse, T.M. Khoshgoftaar, A. Napolitano, and R. Wald. Feature Selection with High-Dimensional Imbalanced Data. In 2009 IEEE International Conference on Data Mining Workshops, pages 507-514. IEEE, 2009.
P. Warnat, R. Eils, and B. Brors. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics, 6(1):265, 2005.
Z. Wu and R.A. Irizarry. Stochastic Models Inspired by Hybridization Theory for Short Oligonucleotide Arrays. J Comput Biol, 12(6):882-893, 2005.