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Advances in Bioinformatics
Volume 2013, Article ID 618461, 8 pages
http://dx.doi.org/10.1155/2013/618461
Research Article

Identification of Robust Pathway Markers for Cancer through Rank-Based Pathway Activity Inference

Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA

Received 30 November 2012; Accepted 19 January 2013

Academic Editor: Hazem Nounou

Copyright © 2013 Navadon Khunlertgit and Byung-Jun Yoon. 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. M. West, C. Blanchette, H. Dressman et al., “Predicting the clinical status of human breast cancer by using gene expression profiles,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 20, pp. 11462–11467, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. L. J. Van't Veer, H. Dai, M. J. Van de Vijver et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, vol. 415, no. 6871, pp. 530–536, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Geman, C. D'Avignon, D. Q. Naiman, and R. L. Winslow, “Classifying gene expression profiles from pairwise mRNA comparisons,” Statistical Applications in Genetics and Molecular Biology, vol. 3, no. 1, article 19, 2004. View at Google Scholar · View at Scopus
  4. 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. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Tian, S. A. Greenberg, S. W. Kong, J. Altschuler, I. S. Kohane, and P. J. Park, “Discovering statistically significant pathways in expression profiling studies,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 38, pp. 13544–13549, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. Z. Guo, T. Zhang, X. Li et al., “Towards precise classification of cancers based on robust gene functional expression profiles,” BMC Bioinformatics, vol. 6, article 58, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Auffray, “Protein subnetwork markers improve prediction of cancer outcome,” Molecular Systems Biology, vol. 3, article 141, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. 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, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. E. Lee, H. Y. Chuang, J. W. Kim, T. Ideker, and D. Lee, “Inferring pathway activity toward precise disease classification,” PLoS Computational Biology, vol. 4, no. 11, Article ID e1000217, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Su, B. J. Yoon, and E. R. Dougherty, “Accurate and reliable cancer classification based on probabilistic inference of pathway activity,” PloS ONE, vol. 4, no. 12, Article ID e8161, 2009. View at Google Scholar · View at Scopus
  11. J. Su, B. J. Yoon, and E. R. Dougherty, “Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network,” BMC Bioinformatics, vol. 11, no. 6, article 8, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. J. A. Eddy, L. Hood, N. D. Price, and D. Geman, “Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC),” PLoS Computational Biology, vol. 6, no. 5, Article ID e1000792, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. N. Khunlertgit and B. J. Yoon, “Finding robust pathway markers for cancer classification,” in Proceedings of the IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS '12), 2012.
  14. 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,” New England Journal of Medicine, vol. 347, no. 25, pp. 1999–2009, 2002. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Desmedt, F. Piette, S. Loi et al., “Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series,” Clinical Cancer Research, vol. 13, no. 11, pp. 3207–3214, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Pawitan, J. Bjohle, L. Amler, and A. L. Borg, “Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts,” Breast Cancer Research, vol. 7, pp. R953–R964, 2005. View at Google Scholar
  17. H. Y. Chang, D. S. A. Nuyten, J. B. Sneddon et al., “Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 10, pp. 3738–3743, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Edgar, M. Domrachev, and A. E. Lash, “Gene Expression Omnibus: NCBI gene expression and hybridization array data repository,” Nucleic Acids Research, vol. 30, no. 1, pp. 207–210, 2002. View at Google Scholar · View at Scopus
  19. R. C. Gentleman, V. J. Carey, D. M. Bates et al., “Bioconductor: open software development for computational biology and bioinformatics,” Genome Biology, vol. 5, no. 10, p. R80, 2004. View at Google Scholar · View at Scopus
  20. A. Liberzon, A. Subramanian, R. Pinchback, H. Thorvaldsdóttir, P. Tamayo, and J. P. Mesirov, “Molecular signatures database (MSigDB) 3.0,” Bioinformatics, vol. 27, no. 12, pp. 1739–1740, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley Interscience, New York, NY, USA, 2006.
  22. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006. View at Publisher · View at Google Scholar · View at Scopus