Table of Contents Author Guidelines Submit a Manuscript
Advances in Bioinformatics
Volume 2009, Article ID 480486, 7 pages
Research Article

A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals

1Epidemiology, Institute of Public Health, University of Southern Denmark, J. B. Winsløws Vej 9B, 5000 Odense C, Denmark
2Department of Biochemistry, Pharmacology and Genetics (BFG), Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark
3Department of Obstetrics and Gynaecology, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark

Received 16 December 2008; Revised 4 May 2009; Accepted 11 May 2009

Academic Editor: Paul Pavlidis

Copyright © 2009 Qihua Tan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Gasca, J. S. Sánchez, and R. Alonso, “Eliminating redundancy and irrelevance using a new MLP-based feature selection method,” Pattern Recognition, vol. 39, no. 2, pp. 313–315, 2006. View at Publisher · View at Google Scholar
  2. A. Dupuy and R. M. Simon, “Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting,” Journal of the National Cancer Institute, vol. 99, no. 2, pp. 147–157, 2007. View at Publisher · View at Google Scholar
  3. M. Zervakis, M. E. Blazadonakis, G. Tsiliki, V. Danilatou, M. Tsiknakis, and D. Kafetzopoulos, “Outcome prediction based on microarray analysis: a critical perspective on methods,” BMC Bioinformatics, vol. 10, article 53, 2009. View at Google Scholar
  4. L. Ein-Dor , I. Kela, G. Getz, D. Givol, and E. Domany, “Outcome signature genes in breast cancer: is there a unique set?” Bioinformatics, vol. 21, pp. 171–178, 2005. View at Google Scholar
  5. B. Selman, “A hard statistical view,” Nature, vol. 451, pp. 639–640, 2008. View at Google Scholar
  6. Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007. View at Publisher · View at Google Scholar
  7. Q. Guo, W. Wu, D. L. Massart, C. Boucon, and S. de Jong, “Feature selection in principal component analysis of analytical data,” Chemometrics and Intelligent Laboratory Systems, vol. 61, no. 1-2, pp. 123–132, 2002. View at Publisher · View at Google Scholar
  8. H.-L. Wei and S. A. Billings, “Feature subset selection and ranking for data dimensionality reduction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 162–166, 2007. View at Publisher · View at Google Scholar
  9. G. H. Golub and C. F. Van Loan, Matrix Computations, Johns Hopkins Press, Baltimore, Md, USA, 3rd edition, 1996.
  10. M. Korenberg, S. A. Billings, Y. P. Liu, and P. J. McIlroy, “Orthogonal parameter estimation algorithm for non-linear stochastic systems,” International Journal of Control, vol. 48, no. 1, pp. 193–210, 1988. View at Google Scholar
  11. L. Bullinger, K. Döhner, E. Bair et al., “Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia,” The New England Journal of Medicine, vol. 350, no. 16, pp. 1605–1616, 2004. View at Publisher · View at Google Scholar
  12. A. H. Bild, G. Yao, J. T. Chang et al., “Oncogenic pathway signatures in human cancers as a guide to targeted therapies,” Nature, vol. 439, no. 7074, pp. 353–357, 2006. View at Publisher · View at Google Scholar
  13. K. M. Jochumsen, Q. Tan, E. V. Høgdall et al., “Gene expression profiling as a prognostic marker in women with ovarian cancer,” International Journal of Gynecological Cancer. In press.
  14. K. J. Livak and T. D. Schmittgen, “Analysis of relative gene expression data using real-time quantitative PCR and the 2ΔΔCT method,” Methods, vol. 25, no. 4, pp. 402–408, 2001. View at Publisher · View at Google Scholar