Fuzzy set-based microarray data analysis techniques for interesting block identification

Authors:
Keon Myung Lee;Kyung Soon Hwang;Chan Hee Lee
Affiliations:
School of Electrical Engineering and Computer Science, Chungbuk National University, Cheongju, Chungbuk, Korea;School of Electrical Engineering and Computer Science, Chungbuk National University, Cheongju, Chungbuk, Korea;Department of Biobiology, Chungbuk National University, Cheongju, Chungbuk, Korea
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

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Abstract

Microarrays are one of biotechnology products which enable to measure the expression level of thousands of genes simultaneously. It is sometimes crucial to identify some interesting blocks from microarray data for further investigation. Due to the massive volume of data, it is desirable to get assistance of software tools to handle this task. This paper introduces three fuzzy set-based microarray data analysis techniques used to find local cluster, to locate contrasting group, and to filter group with specific pattern.