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Journal of Biomedicine and BiotechnologyVolume 2009 2009, Article ID 587405, 7 pages

Research Article

Centre for Bioinformation Science, MSI, The Australian National University, Canberra, ACT 0200, Australia

School of Mathematics and Statistics, Faculty of Science, Prince of Wales Clinical School, Faculty of Medicine, University of NSW, Sydney 2052, Australia

Received 3 March 2009; Revised 31 July 2009; Accepted 3 November 2009

Academic Editor: Satoru Miyano

Copyright © 2009 Yvonne E. Pittelkow and Susan R. Wilson. 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

Scientific advances are raising expectations that patient-tailored treatment will soon be available. The development of resulting clinical approaches needs to be based on well-designed experimental and observational procedures that provide data to which proper biostatistical analyses are applied. Gene expression microarray and related technology are rapidly evolving. It is providing extremely large gene expression profiles containing many thousands of measurements. Choosing a subset from these gene expression measurements to include in a gene expression signature is one of the many challenges needing to be met. Choice of this signature depends on many factors, including the selection of patients in the training set. So the reliability and reproducibility of the resultant prognostic gene signature needs to be evaluated, in such a way as to be relevant to the clinical setting. A relatively straightforward approach is based on cross validation, with separate selection of genes at each iteration to avoid selection bias. Within this approach we developed two different methods, one based on forward selection, the other on genes that were statistically significant in all training blocks of data. We demonstrate our approach to gene signature evaluation with a well-known breast cancer data set.





Author: Yvonne E. Pittelkow and Susan R. Wilson

Source: https://www.hindawi.com/



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