Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context
Gad Abraham, Adam Kowalczyk, Sherene Loi, Izhak Haviv, Justin Zobel
BMC BIOINFORMATICS | BMC | Published : 2010
BACKGROUND: Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from any one study are unstable. It has been suggested that the underlying pathways are essentially identical, and that the expression of gene sets, rather than that of individual genes, may be more informative with respect to prognosis and understanding of the underlying biological process. RESULTS: We sought to examine the stability of prognostic signatures based on gene sets rather than individual genes. We classified breast cancer cases from five microarray studies according to..View full abstract
This work was supported by the Australian Research Council, and by the NICTA Victorian Research Laboratory. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Center of Excellence program.SL is supported by the National Health and Medical Research Council of Australia (NHMRC) and the European Society of Medical Oncology. We thank Justin Bedo for providing R code for SVMs, Raj Gaire for critical reading of the manuscript, and William Webber for discussions of ranking methods.