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The Scientific World Journal
Volume 2014 (2014), Article ID 362141, 13 pages
http://dx.doi.org/10.1155/2014/362141
Research Article

RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes

School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, Australia

Received 5 November 2013; Accepted 11 December 2013; Published 19 January 2014

Academic Editors: P. Chong and P. Van Dam

Copyright © 2014 Ashish Saini 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

Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification. Methods. We propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples. Results. The evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches.