Abstract
Testing for natural selection operating at molecular level has become one of the important issues in contemporary bioinformatics. In the paper the novel methodology called quasi dominance rough set approach (QDRSA) is proposed and applied for testing of balancing selection in four genes involved in human familial cancer. QDRSA can be considered as a hybrid of classical rough set approach (CRSA) and dominance rough set approach (DRSA). The advantages of QDRSA over CRSA and DRSA are illustrated for certain class of problems together with limitations of proposed methodology for other types of problems where CRSA or DRSA are better choice. The analysis of the reasons why QDRSA can produce decision algorithms yielding smaller error rates than DRSA is performed on the real world example, what shows that superiority of QDRSA in certain types of applications is of practical value.
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References
Cyran, K.A.: Modified indiscernibility relation in the theory of rough sets with real-valued attributes: application to recognition of Fraunhofer diffraction patterns. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 14–34. Springer, Heidelberg (2008)
Cyran, K.A.: Classical and dominance based rough sets in the search for genes under balancing selection. In: Transactions on Rough Sets X. LNCS. Springer, Heidelberg (2009) (in press)
Cyran, K.A., Mrozek, A.: Rough sets in hybrid methods for pattern recognition. International Journal of Intelligent Systems 16(2), 149–168 (2001)
Cyran, K.A., Polanska, J., Kimmel, M.: Testing for signatures of natural selection at molecular genes level. Journal of Medical Informatics and Technologies 8, 31–39 (2004)
Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation of preference relation by dominance relations. European Journal of Operational Research 117, 63–83 (1999)
Greco, S., Matarazzo, B., Slowinski, R., Stefanowski, J.: Variable consistency model of Dominance-based Rough Sets Approach. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS, vol. 2005, pp. 170–181. Springer, Heidelberg (2001)
Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge (1983)
Pawlak, Z.: Rough sets. International Journal of Information and Computer Sciences 11, 341–356 (1982)
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1992)
Peters, J.F.: Near sets. general theory about nearness of objects. Applied Mathematical Sciences 1(53), 2609–2629 (2007)
Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity. IEEE Transactions on Data and Knowledge Engineering 12(2), 331–336 (2000)
Zadeh, L.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Zhang, J.: Evolution of the Human ASPM gene, a major determinant of brain size. Genetics 165, 2063–2070 (2003)
Ziarko, W.: Variable precision rough sets model. Journal of Computer and Systems Sciences 46(1), 39–59 (1993)
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Cyran, K.A. (2009). Quasi Dominance Rough Set Approach in Testing for Traces of Natural Selection at Molecular Level. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_16
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DOI: https://doi.org/10.1007/978-3-642-00563-3_16
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