On the Role and Potential of High-Dimensional Biologic Data in Cancer Research

Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


Chronic Myeloid Leukemia International HapMap Consortium Breast Cancer Susceptibility Locus Comparative Genomic Hybridiza Autoantibody Signature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Amundadottir, L., Sulem, P., Gudmundsson, J., et al. (2006). A common variant associated with prostate cancer in european and african populations. Nature Genetics, 38(6):652–658.PubMedCrossRefGoogle Scholar
  2. Benjamini, Y. and Hochberg, Y. (1995). Controlling for false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57:289–300.Google Scholar
  3. Druker, B. J., Guilhot, F., O’Brien, S. G., et al. (2006). Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. New England Journal of Medicine, 355(23):2408–2417.PubMedCrossRefGoogle Scholar
  4. Easton, D., Pooley, K., Dunning, A., et al. (2007). Genome-wide association study identifies novel breast cancer susceptibility loci. Nature, 447(7148): 1087–1093.PubMedCrossRefGoogle Scholar
  5. Efron, B. (2004). Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. Journal of the American Statistical Association, 99:96–104.CrossRefGoogle Scholar
  6. Faca, V., Coram, M., Phanstiel, D., et al. (2006). Quantitative analysis of acrylamide labeled serum proteins by lc-ms/ms. Journal of Proteome Research, 5(8):2009–2018.PubMedCrossRefGoogle Scholar
  7. Felsenstein, J. (2007). Theoretical Evolutionary Genetics. University of Washington/ASUW Publishing, Seattle, WA.Google Scholar
  8. Freedman, M. L., Haiman, C. A., Patterson, N., et al. (2006). Admixture mapping identifies 8q24 as a prostate cancer risk locus in african-american men. Proceedings of the National Academy of Sciences, 103(38):14068–14073.CrossRefGoogle Scholar
  9. Golub, T. R., Slonim, D. K., Tamayo, P., et al. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286(5439):531–537.PubMedCrossRefGoogle Scholar
  10. Hinds, D. A., Stuve, L. L., Nilsen, G. B., et al. (2005). Whole-genome patterns of common DNA variation in three human populations. Science, 307(5712):1072–1079.PubMedCrossRefGoogle Scholar
  11. Hunter, D. J., Kraft, P., Jacobs, K. B., et al. (2007). A genome-wide association study identifies alleles in fgfr2 associated with risk of sporadic postmenopausal breast cancer. Nature Genetics, 39(6):870–874.PubMedCrossRefGoogle Scholar
  12. Khatri, P. and Draghici, S. (2005). Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics, 18:3587–3595.CrossRefGoogle Scholar
  13. Ott, J. (1991). Analysis of Human Genetic Linkage. Johns Hopkins University Press, Baltimore, MD.Google Scholar
  14. Piccart-Gebhart, M. J., Procter, M., Leyland-Jones, B., et al. (2005). Trastuzumab after adjuvant chemotherapy in her2-positive breast cancer. New England Journal of Medicine, 353(16):1659–1672.PubMedCrossRefGoogle Scholar
  15. Prentice, R. and Qi, L. (2006). Aspects of the design and analysis of high-dimensional snp studies for disease risk estimation. Biostatistics, 7:339–354.PubMedCrossRefGoogle Scholar
  16. Rouzier, R., Perou, C. M., Symmans, W. F., et al. (2005). Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clinical Cancer Research, 11(16):5678–5685.PubMedCrossRefGoogle Scholar
  17. Ruczinski, I., Kooperberg, C., and LeBlanc, M. (2003). Logic regression. Journal of Computational and Graphical Statististics, 12:475–511.CrossRefGoogle Scholar
  18. Samani, N. J., Erdmann, J., Hall, A. S., et al. (2007). Genomewide association analysis of coronary artery disease. New England Journal of Medicine, 357(5):443–453.PubMedCrossRefGoogle Scholar
  19. Shurubor, Y., Matson, W., Martin, R., et al. (2005). Relative contribution of specific sources of systematic errors and analytic imprecision to metabolite analysis by hplc-ecd. Metabolomics, 1:159–168.CrossRefGoogle Scholar
  20. The International HapMap Consortium (2003). The international hapmap project. Nature, 426(6968):789–796.Google Scholar
  21. The Women's Health Initiative Steering Committee (2004). Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: The women's health initiative randomized controlled trial. JAMA, 291(14):1701–1712. Google Scholar
  22. Thomas, D. (2004). Statistical Methods in Genetic Epidemiology. Oxford University Press, London.Google Scholar
  23. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B, 58:267–288.Google Scholar
  24. Wang, X., Yu, J., Sreekumar, A., et al. (2005). Autoantibody signatures in prostate cancer. New England Journal of Medicine, 353(12):1224–1235.PubMedCrossRefGoogle Scholar
  25. Writing Group for the Women's Health Initiative Investigators (2002). Risks and benefits of estrogen plus progestin in healthy postmenopausal women: Principal results from the women's health initiative randomized controlled trial. Journal of the American Medical Association, 288(3):321–333.Google Scholar
  26. Yeager, M., N., O., Hayes, R. B., et al. (2007). Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nature Genetics, 39(5):645–649.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Division of Public Health SciencesDivision of Public Health SciencesSeattleUSA

Personalised recommendations