Minimum Redundancy Gene Selection Based on Grey Relational Analysis

Authors:
Li-Juan Zhang;Zhou-Jun Li;Huo-Wang Chen;Jian Wen
Affiliations:
National Laboratory for Parallel and Distributed Processing, Changsha 410073 China;Beihang University, Beijing 100083 China;National Laboratory for Parallel and Distributed Processing, Changsha 410073 China;National Laboratory for Parallel and Distributed Processing, Changsha 410073 China
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

Citing 0
Cited 1

An effective gene selection method based on relevance analysis and discernibility matrix

PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

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.