Title

SDED: A novel filter method for cancer-related gene selection

Authors

Wenlong Xu¹, Minghui Wang², Xianghua Zhang¹, Lirong Wang¹, Huanqing Feng^{1, *}

Affiliation

¹Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China; ²The College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100022, China

Email

hqfeng@ustc.edu.cn
 

86 551 3601800; * Corresponding author

Article Type

Hypothesis

Date

received December 19, 2007; revised March 13, 2008; accepted April 04, 2008; published April 11, 2008

Abstract

Gene selection is to detect the most significantly expressed genes under different conditions expression data. The current challenge in gene selection is the comparison of a large number of genes with limited patient samples. Thus it is trivial task in simple statistical analysis. Various statistical measurements are adopted by filter methods applied in gene selection studies. Their ability to discriminate phenotypes is crucial in classification and selection. Here we describe the standard deviation error distribution (SDED) method for gene selection. It utilizes variations within-class and among-class in gene expression data. We tested the method using 4 leukemia datasets available in the public domain. The method was compared with the GS2 and CHO methods. The Prediction accuracies by SDED are better than both GS2 and CHO for different datasets. These are 0.8-4.2% and 1.6-8.4% more that in GS2 and CHO. The related OMIM annotations and KEGG pathways analyses verified that SDED can pick out more 4.0% and 6.1% genes with biological significance than GS2 and CHO, respectively.

Keywords

gene selection; filter method; support vector machine; SDED

Citation

Xu et al., Bioinformation 2(7): 301-303 (2008)

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.