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.
Bioinformation