Browse > Article
http://dx.doi.org/10.5391/JKIIS.2009.19.4.545

Microarray Data Retrieval Using Fuzzy Signature Sets  

Lee, Sun-A (충북대학교 전자정보대학 컴퓨터공학부)
Lee, Keon-Myung (충북대학교 전자정보대학 컴퓨터공학부)
Ryu, Keun-Ho (충북대학교 전자정보대학 컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.19, no.4, 2009 , pp. 545-549 More about this Journal
Abstract
Microarray data sets could contain thousands of gene expression levels and have been considered as an important source from which meaningful patterns could be extracted for further analysis in biological studies. It is sometimes necessary to retrieve out specific genes or samples of analyst's interest in an effective way. This paper is concerned with a method to make use of fuzzy signature set in order to filter out genes or samples which satisfy complicated constraints as well as simple ones. Fuzzy signatures are an extension of vector valued fuzzy sets, in which elements of the vector are allowed to have a vector. Fuzzy signature sets are similar to fuzzy signatures except that their leaf elements are fuzzy sets defined on the interval [0,1]. This paper introduces an extension of fuzzy signature sets which specifies aggregation operators at each internal node and comparison operators for aggregation. It also shows how to use the extended fuzzy signature sets in microarray data retrieval and some examples of its usage.
Keywords
fuzzy pattern matching; microarray; data analysis; bioinformatics;
Citations & Related Records
연도 인용수 순위
  • Reference
1 K. Tamas, L. T. Koczy, Mamdani-type inference in Fuzzy Signature based Rule Bases, Proc. of the 8th Int. Symp. of Hugarian Researchers on Computational Intelligence and Informatics, pp.513-525, 2007
2 K. W. Wong, T. D. Gedeon, L. T. Koczy, Fuzzy Signature and Congnitive Modeling for Complex Decision Model, Theoretical Advances and Applications of Fuzzy Logic, ASC42, pp.380-389, 2007
3 R. Yager, On ordered weigthed averaging aggregation operators in multicriteria decision making, IEEE Trans. on Systems, Man and Cybernetics, Vol.18, No.1, pp.183-190, 1988   DOI   ScienceOn
4 T. Vamos, L.T. Koczy, G. Biro, Fuzzy signature in data mining, Proc. of IFSA World Congress and 20th NAIPS Int. Conf, pp.2842-2846, 2001
5 D. Filev, R. Yager, On the issue of objectioning OWA operator weights, Fuzzy Sets and Systems, Vol.94, No.2, pp.157-169, 1998   DOI   ScienceOn
6 K. W. Wong, A. Chong, T. D. Gedeon, L. T. Koczy, T. Vamos, Hierarchical Fuzzy Signature Structure for Complex Structured Data, Proc. of Int. Symp. on Computational Intelligence and Intelligent Informatics, pp.105-109, 2003
7 L. T. Koczy, T. Vamos, G. Biro, Fuzzy Signatures, Proc. of EUROFUSE-SIC'99, pp.210-217, 1999
8 B.S.U. Mendis, T.D. Gedeon, Aggregation Selection for Hierarchical Fuzzy Signatures: A Comparison of Hierarchical OWA ad WRAO, Proc. of IPMU'08, pp.1376-1383, 2008
9 B.S.U. Mendis, T .D. Gedeon, J. Botzheim, L.T. Koczy, Generalized Weighted Relevance Aggregation Operators For Hierarchical Fuzzy Signatures, Proc. of Int. Conf. on Computational Intelligence for Modeling Control and Automation, 2006