Identification of cancer diagnosis estimation models using evolutionary algorithms: A case study for breast cancer, melanoma, and cancer in the respiratory system

Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication

Volumn , Issue , 2011, Pages 503-510

  Winkler, Stephan M ^a   Affenzeller, Michael ^a   Jacak, Witold ^a   Stekel, Herbert ^b  

^a UNIVERSITY OF APPLIED SCIENCES UPPER AUSTRIA   (Austria)

^b KEPLER UNIVERSITY HOSPITAL   (Austria)

Author keywords

cancer diagnosis estimation; data mining; machine learning; statistical analysis; tumor marker data

Indexed keywords

BLOOD PARAMETERS; BREAST CANCER; CANCER DIAGNOSIS; CANCER DIAGNOSIS ESTIMATION; DATA RECORDS; DATA-BASED MODELING; EMPIRICAL RESEARCH; ESTIMATION MODELS; K-NEAREST NEIGHBORS; MACHINE-LEARNING; TEST CASE; TEST SAMPLES; TUMOR MARKER DATA; TUMOR MARKERS;

DERMATOLOGY; ENGINEERING RESEARCH; ESTIMATION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; MATHEMATICAL MODELS; NEURAL NETWORKS; ONCOLOGY; PROGRAM DIAGNOSTICS; RESPIRATORY MECHANICS; RESPIRATORY SYSTEM; TUMORS;

DISEASES;

EID: 80051922252     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2001858.2002040     Document Type: Conference Paper

References (37)

1. M. Affenzeller and S. Wagner. SASEGASA: A new generic parallel evolutionary algorithm for achieving highest quality results. Journal of Heuristics - Special Issue on New Advances on Parallel Meta-Heuristics for Complex Problems, 10:239-263, 2004.
2. M. Affenzeller, S. Winkler, S. Wagner, and A. Beham. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Chapman & Hall / CRC, 2009.
3. E. Alba, J. G.-N. L. Jourdan, and E.-G. Talbi. Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. IEEE Congress on Evolutionary Computation 2007, pages 284-290, 2007.
4. G. L. Andriole, E. D. Crawford, R. L. Grubband, S. S. Buys, D. Chia, T. R. Church, et al. Mortality results from a randomized prostate-cancer screening trial. New England Journal of Medicine, 360(13):1310-1319, 2009.
5. W. Banzhaf and C. Lasarczyk. Genetic programming of an algorithmic chemistry. In U. O'Reilly, T. Yu, R. Riolo, and B. Worzel, editors, Genetic Programming Theory and Practice II, pages 175-190. Ann Arbor, 2004.
6. N. Bitterlich and J. Schneider. Cut-off-independent tumour marker evaluation using ROC approximation. Anticancer Research, 27:4305-4310, 2007.
7. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
8. N. Clegg, C. Ferguson, L. True, H. Arnold, A. Moorman, J. Quinn, R. Vessella, and P. Nelson. Molecular characterization of prostatic small-cell neuroendocrine carcinoma. Prostate, 55(1):55-64, 2003. (Pubitemid 36397745)
9. G. Crombach, H. Würz, F. Herrmann, R. Kreienberg, V. Möbus, P. Schmidt-Rhode, G. Sturm, H. Caffier, and H. Kaesemann. The importance of the scc antigen in the diagnosis and follow-up of cervix carcinoma. Deutsche Medizinische Wochenschrift, 114(18):700-705, 1989. (Pubitemid 19127259)
10. nd edition, 2000.
11. M. J. Duffy and J. Crown. A personalized approach to cancer treatment: how biomarkers can help. Clinical Chemistry, 54(11):1770-1779, 2008.
12. A. Eiben and J. Smith. Introduction to Evolutionary Computation. Natural Computing Series. Springer-Verlag Berlin Heidelberg, 2003.
13. B. Frey, R. Morant, H. Senn, and W. Riesen. Clinical assessment of the new tumor marker tps. International Journal for Cancer Research and Treatment, 17:270-276, 1994. (Pubitemid 24231528)
14. P. Gold and S. O. Freedman. Demonstration of tumor-specific antigens in human colonic carcinomata by immunological tolerance and absorption techniques. The Journal of Experimental Medicine, 121:439-462, 1965.
15. J. H. Holland. Adaption in Natural and Artifical Systems. University of Michigan Press, 1975.
16. J. A. Koepke. Molecular marker test standardization. Cancer, 69:1578-1581, 1992.
17. R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on Artificial intelligence, volume 2, pages 1137-1143. Morgan Kaufmann, 1995.
18. H. Koprowski, M. Herlyn, Z. Steplewski, and H. Sears. Specific antigen in serum of patients with colon carcinoma. Science, 212(4490):53-55, 1981. (Pubitemid 11114676)
19. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, 1992.
20. th edition, 2008.
21. R. S. Lai, C. C. Chen, P. C. Lee, and J. Y. Lu. Evaluation of cytokeratin 19 fragment (cyfra 21-1) as a tumor marker in malignant pleural effusion. Japanese Journal of Clinical Oncology, 29(9):421-424, 199.
22. L. Ljung. System Identification - Theory For the User, 2nd edition. PTR Prentice Hall, Upper Saddle River, N.J., 1999.
23. G. J. Mizejewski. Alpha-fetoprotein structure and function: relevance to isoforms, epitopes, and conformational variants. Experimental biology and medicine, 226(5):377-408, 2001.
24. O. Nelles. Nonlinear System Identification. Springer Verlag, Berlin Heidelberg New York, 2001.
25. Y. Niv. Muc1 and colorectal cancer pathophysiology considerations. World Journal of Gastroenterology, 14(14):2139-2141, 2008.
26. D. Nonaka, L. Chiriboga, and B. Rubin. Differential expression of s100 protein subtypes in malignant melanoma, and benign and malignant peripheral nerve sheath tumors. Journal of Cutaneous Pathology, 35(11):1014-1019, 2008.
27. A. J. Rai, Z. Zhang, J. Rosenzweig, I. ming Shih, T. Pham, E. T. Fung, L. J. Sokoll, and D. W. Chan. Proteomic approaches to tumor marker discovery. Archives of Pathology & Laboratory Medicine, 126(12):1518-1526, 2002.
28. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
29. S. Wagner. Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Johannes Kepler University Linz, 2009.
30. th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) 2005, volume 4, pages 76-81. International Institute of Informatics and Systemics, 2005.
31. th edition, 1989.
32. S. Winkler. Evolutionary System Identification - Modern Concepts and Practical Applications. PhD thesis, Institute for Formal Models and Verification, Johannes Kepler University Linz, 2008.
33. S. Winkler, M. Affenzeller, W. Jacak, and H. Stekel. Classification of tumor marker values using heuristic data mining methods. In Proceedings of the GECCO 2010 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC 2010), 2010.
34. S. Winkler, M. Affenzeller, G. Kronberger, M. Kommenda, S. Wagner, W. Jacak, and H. Stekel. Feature selection in the analysis of tumor marker data using evolutionary algorithms. In Proceedings of the 7th International Mediterranean and Latin American Modelling Multiconference, pages 1-6, 2010.
35. B. W. Yin, A. Dnistrian, and K. O. Lloyd. Ovarian cancer antigen CA125 is encoded by the MUC16 mucin gene. International Journal of Cancer, 98(5):737-40, 2002. (Pubitemid 34225625)
36. K. Yonemori, M. Ando, T. S. Taro, N. Katsumata, K. Matsumoto, Y. Yamanaka, T. Kouno, C. Shimizu, and Y. Fujiwara. Tumor-marker analysis and verification of prognostic models in patients with cancer of unknown primary, receiving platinum-based combination chemotherapy. Journal of Cancer Research and Clinical Oncology, 132(10):635-642, 2006. (Pubitemid 44310329)
37. L. Zhong, X. Zhou, K. Wei, X. Yang, C. Ma, C. Zhang, and Z. Zhang. Application of serum tumor markers and support vector machine in the diagnosis of oral squamous cell carcinoma. Shanghai Kou Qiang Yi Xue (Shanghai Journal of Stomatology), 17(5):457-460, 2008.