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Journal of Biomedicine and Biotechnology
Volume 2010, Article ID 726413, 12 pages
http://dx.doi.org/10.1155/2010/726413
Research Article

Neighborhood Rough Set Reduction-Based Gene Selection and Prioritization for Gene Expression Profile Analysis and Molecular Cancer Classification

1Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China
2Department of Biology, University of Science and Technology of China, Hefei, Anhui 230027, China
3School of Computer and Communication, Hunan University, Changsha, Hunan, Anhui 410082, China
4Department of Information, Electronic Engineering Institute, Hefei 230037, China

Received 11 October 2009; Accepted 7 April 2010

Academic Editor: Ying Xu

Copyright © 2010 Mei-Ling Hou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers.