Page 374 - Special Topic Session (STS) - Volume 4
P. 374

STS1080 Mahmoud Rafea et al.
                      Journal of Innovative Research in Science, Engineering and Technology,
                      4(6), 255-259.
                  8.  Fahrudin, T. M., Syarif, I., & Barakbah, A. R. (2017, September).
                      Discovering patterns of NED-breast cancer based on association rules
                      using apriori and FP-growth. In Knowledge Creation and Intelligent
                      Computing (IES-KCIC), 2017 International Electronics Symposium on (pp.
                      132-139). IEEE.
                  9.  Poorani, S., Balasubramanie, P., & Kumar, D. V. Apriori algorithm for
                      identifying the association rules between clinical traits of asthma.
                  10. Aebersold, R., Anderson, L., Caprioli, R., Druker, B., Hartwell, L. and Smith,
                      R., 2005. Perspective: a program to improve protein biomarker discovery
                      for cancer. Journal of proteome research, 4(4), pp.1104-1109.
                  11. Hanash, S., 2004. Moving forward with clinical proteomics. Clinical
                      Proteomics, 1(1), p.3.
                  12. Mischak, H., Apweiler, R., Banks, R.E., Conaway, M., Coon, J., Dominiczak,
                      A., Ehrich, J.H., Fliser, D., Girolami, M., Hermjakob, H. and Hochstrasser, D.,
                      2007. Clinical proteomics: a need to define the field and to begin to set
                      adequate standards. PROTEOMICS–Clinical Applications, 1(2), pp.148-
                      156.
                  13. Barnes, M.R. and Gray, I.C. eds., 2003. Bioinformatics for geneticists. John
                      Wiley & Sons.
                  14. Timms, J.F., Hale, O.J. and Cramer, R., 2016. Advances in mass
                      spectrometry-based cancer research and analysis: from cancer
                      proteomics to clinical diagnostics. Expert review of proteomics, 13(6),
                      pp.593-607.
                  15. Wang, H., Shi, T., Qian, W.J., Liu, T., Kagan, J., Srivastava, S., Smith, R.D.,
                      Rodland, K.D. and Camp, D.G., 2016. The clinical impact of recent
                      advances in LC-MS for cancer biomarker discovery and verification.
                      Expert review of proteomics, 13(1), pp.99-114.
                  16. Pasini, E.M., Mann, M. and Thomas, A.W., 2010. Red blood cell
                      proteomics. Transfusion clinique et biologique, 17(3), pp.151-164.
                  17. Bryk, A.H. and Wiśniewski, J.R., 2017. Quantitative analysis of human red
                      blood cell proteome. Journal of proteome research, 16(8), pp.2752-2761.
                  18. Jain, K.K. and Jain, K.K., 2010. The handbook of biomarkers (pp. 23-72).
                      New York: Springer.
                  19. Rifai, N., Gillette, M.A. and Carr, S.A., 2006. Protein biomarker discovery
                      and validation: the long and uncertain path to clinical utility. Nature
                      biotechnology, 24(8), p.971.
                  20. Vasan, R.S., 2006. Biomarkers of cardiovascular disease: molecular basis
                      and practical considerations. Circulation, 113(19), pp.2335-2362.
                  21. Gerszten, R.E. and Wang, T.J., 2008. The search for new cardiovascular
                      biomarkers. Nature, 451(7181), p.949.



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