Title :
Minimum Redundancy Gene Selection Based on Grey Relational Analysis
Author :
Zhang, Li-Juan ; Li, Zhou-Jun ; Chen, Huo-Wang ; Wen, Jian
Abstract :
In this article we describe a method for selecting informative genes from microarray data. The method is based on clustering, namely, it first find similar genes, group them and then select informative genes from these groups to avoid redundancy. A new gene similarity measure based on grey relational analysis (GRA), called grey relational grade (GRG), is used in clustering. Experiments on three public data sets demonstrate the effectiveness of our method
Keywords :
genetics; grey systems; pattern clustering; gene similarity measure; grey relational analysis; grey relational grade; informative genes; microarray data; minimum redundancy gene selection; Computer science; Conferences; Data analysis; Data engineering; Data mining; Distributed processing; Gene expression; Laboratories; Mutual information; Signal to noise ratio;
Conference_Titel :
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2702-7
DOI :
10.1109/ICDMW.2006.108
