Classification of tumor marker values using heuristic data mining methods

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication

Volumn , Issue , 2010, Pages 1915-1922

  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

Classification; Data mining; Machine learning; Statistical analysis; Tumor marker data

Indexed keywords

AUSTRIA; BINARY CLASSIFICATION PROBLEMS; BODY TISSUES; CLASSIFICATION; DATA MINING METHODS; DATA-BASED MODELING; DESIGN CLASSIFIERS; MACHINE-LEARNING; MEDICAL DATABASE; MEDICAL MODELING; NON-LINEARITY; OVERFITTING; STATISTICAL ANALYSIS; TUMOR MARKER DATA; TUMOR MARKERS;

BLOOD; CLASSIFICATION (OF INFORMATION); DATA HANDLING; DATA MINING; GENETIC PROGRAMMING; HEURISTIC METHODS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MEDICAL COMPUTING; MINING; STATISTICAL METHODS;

TUMORS;

EID: 77955932153     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830761.1830826     Document Type: Conference Paper

References (35)

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 and S. Wagner. Offspring selection: A new self-adaptive selection scheme for genetic algorithms. In B. Ribeiro, R. F. Albrecht, A. Dobnikar, D. W. Pearson, and N. C. Steele, editors, Adaptive and Natural Computing Algorithms, Springer Computer Science, pages 218-221. Springer, 2005.
3. M. Affenzeller, S. Wagner, and S. Winkler. Goal-oriented preservation of essential genetic information by offspring selection. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2005, volume 2, pages 1595-1596. Association for Computing Machinery (ACM), 2005.
4. M. Affenzeller, S. Winkler, S. Wagner, and A. Beham. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Chapman & Hall/CRC, 2009.
5. 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.
6. 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.
7. N. Bitterlich and J. Schneider. Cut-off-independent tumour marker evaluation using roc approximation. Anticancer Research, 27:4305-4310, 2007.
8. 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.
9. nd edition, 2000.
10. M. J. Duffy and J. Crown. A personalized approach to cancer treatment: how biomarkers can help. Clinical Chemistry, 54(11):1770-1779, 2008.
11. P. Gill, W. Murray, and M. Wright. Practical Optimization. Academic Press, 1982.
12. 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.
13. S. Hammarstrom. The carcinoembryonic antigen (cea) family: structures, suggested functions and expression in normal and malignant tissues. Seminars in Cancer Biology, 9:67-81, 1999.
14. J. A. Koepke. Molecular marker test standardization. Cancer, 69:1578-1581, 1992.
15. R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. pages 1137-1143. Morgan Kaufmann, 1995.
16. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, 1992.
17. th edition, 2008.
18. 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.
19. L. Ljung. System Identification - Theory For the User, 2nd edition. PTR Prentice Hall, Upper Saddle River, N.J., 1999.
20. G. J. Mizejewski. Alpha-fetoprotein structure and function: relevance to isoforms, epitopes, and conformational variants. Experimental biology and medicine, 226(5):377-408, 2001.
21. K. Morik, M. Imhoff, P. Brockhausen, T. Joachims, and U. Gather. Knowledge discovery and knowledge validation in intensive care. Artificial Intelligence in Medicine, 19:225-249, 2000.
22. O. Nelles. Nonlinear System Identification. Springer Verlag, Berlin Heidelberg New York, 2001.
23. Y. Niv. Muc1 and colorectal cancer pathophysiology considerations. World Journal of Gastroenterology, 14(14):2139-2141, 2008.
24. N. Osman, N. O'Leary, E. Mulcahy, N. Barrett, F. Wallis, K. Hickey, and R. Gupta. Correlation of serum ca125 with stage, grade and survival of patients with epithelial ovarian cancer at a single centre. Irish Medical Journal, 101(8):245-247, 2008.
25. 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.
26. nd edition, 2003.
27. I. M. Thompson, D. K. Pauler, P. J. Goodman, C. M. Tangen, et al. Prevalence of prostate cancer among men with a prostate-specific antigen level < =4.0 ng per milliliter. New England Journal of Medicine, 350(22):2239-2246, 2004.
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. 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.
34. 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.
35. 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.