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BioMed Research International
Volume 2014, Article ID 459203, 15 pages
http://dx.doi.org/10.1155/2014/459203
Research Article

Breast Cancer Prognosis Risk Estimation Using Integrated Gene Expression and Clinical Data

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

Received 2 November 2013; Revised 11 January 2014; Accepted 2 March 2014; Published 14 May 2014

Academic Editor: Brian Oliver

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.

Supplementary Material

The Supplementary Material consists of six files, including Table S1, S2, S3, and S4 and Figure S1 and S2. The Table S1 shows the proposed algorithm based prognostic gene signature. The Table S2 shows the prognosis based average classification results of IPRE algorithm with different cut-off points. The Table S3 and S4 shows the enriched gene ontology (GO) terms and the enriched pathways for the genes in the IPRE gene signature. At last, the Figure S1 shows the boxplot of the IPRE algorithm, and the Figure S2 shows the KEGG pathway of a cell cycle. These supplementary files further supports that the IPRE gene signature has its advantage in classifying two prognosis groups for breast cancer patients effectively.

  1. Supplementary Material