Title

Effective feature selection framework for cluster analysis of microarray data

Authors

Gouchol Pok¹, Jyh-Charn Steve Liu², Keun Ho Ryu^3*

Affiliation

¹Yanbian University of science and Technology, Dept. of Computer Science, Yanji, Jilin, China 133000; ²Texas A&M University, Dept. of Computer Science, College Station, TX, USA; ³Chungbuk National University, DB Bioinformatics Lab, Cheongju, Chungbuk, Korea.

Email

khryu@dblab.chungbuk.ac.kr

Article Type

Hypothesis

Date

Received May 25, 2009; Revised February 18, 2010; Accepted February 24, 2010; Published February 28, 2010

Abstract

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.

Keywords

gene expression microarray, feature selection, classification, clustering

Citation

Pok et al., Bioinformation 4(8): 385-389 (2010)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.