Effective feature selection framework for cluster analysis of microarray data.
The microarray technique has become a standard means in simultaneously examining expression of all genes measured in different circumstances. As microarray data are typically characterized by high dimensional features with a small number of samples, feature selection needs to be incorporated to identify a subset of genes that are meaningful for biological interpretation and accountable for the sample variation. In this article, we present a simple, yet effective feature selection framework suitable for two-dimensional microarray data. Our correlation-based, nonparametric approach allows compact representation of class-specific properties with a small number of genes. We evaluated our method using publicly available experimental data and obtained favorable results.
Yanbian University of science and Technology, Dept. of Computer Science, Yanji, Jilin, China 133000.
Gouchol
G
Pok
Jyh-Charn Steve
JC
Liu
Keun Ho
KH
Ryu