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Minimum Redundancy Gene Selection Based on Grey Relational Analysis
Hong Kong, China December 18-December 22
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.108Sixth IEEE International Conference o ...
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Li-Juan Zhang, National Laboratory for Parallel and Distributed Processing, Changsha 410073 China
Zhou-Jun Li, Beihang University, Beijing 100083 China
Huo-Wang Chen, National Laboratory for Parallel and Distributed Processing, Changsha 410073 China
Jian Wen, National Laboratory for Parallel and Distributed Processing, Changsha 410073 China
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.
Citation:
Li-Juan Zhang, Zhou-Jun Li, Huo-Wang Chen, Jian Wen, "Minimum Redundancy Gene Selection Based on Grey Relational Analysis," icdmw, pp.120-124, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
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