Simpler evaluation of predictions and signature stability for gene expression data

Journal of Biomedicine and Biotechnology

Volumn 2009, Issue , 2009, Pages

  Pittelkow, Yvonne E ^a   Wilson, Susan R ^a,b  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOLOGY; BREAST TUMOR; FEMALE; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METHODOLOGY; PATHOLOGY; PROPORTIONAL HAZARDS MODEL; REPRODUCIBILITY; STANDARD;

BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; FEMALE; GENE EXPRESSION PROFILING; HUMANS; PROPORTIONAL HAZARDS MODELS; REPRODUCIBILITY OF RESULTS;

EID: 77950351400     PISSN: 11107243     EISSN: 11107251     Source Type: Journal    
DOI: 10.1155/2009/587405     Document Type: Article

References (23)

1. M. Schena, Microarray Analysis, Wiley-Liss, New York, NY, USA, 2003.
2. S. Michiels, S. Koscielny, and C. Hill, "Prediction of cancer outcome with microarrays: a multiple random validation strategy," The Lancet, vol. 365, no. 9458, pp. 488-492, 2005. (Pubitemid 40215731)
3. S. G. Baker and B. S. Kramer, "Identifying genes that contribute most to good classification in microarrays," BMC Bioinformatics, vol. 7, article 407, pp. 1-7, 2006.
4. C. Ambroise and G. J. McLachlan, "Selection bias in gene extraction on the basis of microarray gene-expression data," Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 10, pp. 6562-6566, 2002. (Pubitemid 34526172)
5. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, Springer, New York, NY, USA, 2001.
6. R. Simon, "Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data," British Journal of Cancer, vol. 89, no. 9, pp. 1599-1604, 2003. (Pubitemid 37464126)
7. T.-K. Jenssen and E. Hovig, "Gene-expression profiling in breast cancer," The Lancet, vol. 365, no. 9460, pp. 634-635, 2005. (Pubitemid 40260872)
8. R. Tibshirani, "Immune signatures in follicular lymphoma," The New England Journal of Medicine, vol. 352, no. 14, pp. 1496-1497, 2005.
9. Y. Wang, J. G. M. Klijn, Y. Zhang, et al., "Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer," The Lancet, vol. 365, no. 9460, pp. 671-679, 2005. (Pubitemid 40260888)
10. C. J. Scarlett, R. C. Smith, A. Saxby, et al., "Proteomic classification of pancreatic adenocarcinoma tissue using protein chip technology," Gastroenterology, vol. 130, no. 6, pp. 1670-1678, 2006. (Pubitemid 43668861)
11. R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2005.
12. Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo.
13. J. Downward, "Cancer biology: signatures guide drug choice," Nature, vol. 439, no. 7074, pp. 274-275, 2006. (Pubitemid 43128841)
14. J. Fan and J. Lv, "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society B, vol. 70, no. 5, pp. 849-911, 2008.
15. J. M. Neuhaus, "Misspecification," in Encyclopedia of Biostatis-tics, P. Armitage and T. Colton, Eds., vol. 5, pp. 3305-3307, John Wiley & Sons, Chichester, UK, 2004.
16. 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, no. 2, pp. 171-178, 2005. (Pubitemid 40202018)
17. I. A. Wood, P. M. Visscher, and K. L. Mengersen, "Classification based upon gene expression data: bias and precision of error rates," Bioinformatics, vol. 23, no. 11, pp. 1363-1370, 2007. (Pubitemid 47050518)
18. G. J. McLachlan, J. Chevelu, and J. Zhu, "Correcting for selection bias via cross-validation in the classification of microarray data," in Beyond Parametrics in Interdisciplinary Research: A Festschrift to P.K. Sen, N. Balakrishnan, E. Pena, and M. J. Silvapulle, Eds., IMS Lecture Notes-Monograph Series, pp. 383-395, Institute of Mathematical Statistics, Hayward, Calif, USA, 2007.
19. B. Ripley, "Computer intensive methods," in Encyclopedia of Biostatistics, P. Armitage and T. Colton, Eds., vol. 2, pp. 1071-1075, John Wiley & Sons, Chichester, UK, 2004.
20. E. Biganzoli, N. Lama, F. Ambrogi, L. Antolini, and P. Boracchi, "Prediction of cancer outcome with microarrays," The Lancet, vol. 365, no. 9472, pp. 1683-1686, 2005.
21. H.-Y. Chuang, E. Lee, Y.-T. Liu, D. Lee, and T. Ideker, "Network-based classification of breast cancer metastasis," Molecular Systems Biology, vol. 3, article 140, pp. 1-10, 2007.
22. M. J. van de Vijver, Y. D. He, L. J. van 't Veer, et al., "A gene-expression signature as a predictor of survival in breast cancer," The New England Journal of Medicine, vol. 347, no. 25, pp. 1999-2009, 2002. (Pubitemid 35461656)
23. A. Abdullah-Sayani, J. M. Bueno-de-Mesquita, and M. J. van de Vijver, "Technology insight: tuning into the genetic orchestra using microarrays-limitations of DNA microarrays in clinical practice," Nature Clinical Practice Oncology, vol. 3, no. 9, pp. 501-516, 2006. (Pubitemid 44340366)