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Advances in Bioinformatics
Volume 2009 (2009), Article ID 480486, 7 pages
Research Article

A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals

1Epidemiology, Institute of Public Health, University of Southern Denmark, J. B. Winsløws Vej 9B, 5000 Odense C, Denmark
2Department of Biochemistry, Pharmacology and Genetics (BFG), Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark
3Department of Obstetrics and Gynaecology, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark

Received 16 December 2008; Revised 4 May 2009; Accepted 11 May 2009

Academic Editor: Paul Pavlidis

Copyright © 2009 Qihua Tan 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.


Different from significant gene expression analysis which looks for genes that are differentially regulated, feature selection in the microarray-based prognostic gene expression analysis aims at finding a subset of marker genes that are not only differentially expressed but also informative for prediction. Unfortunately feature selection in literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significances. We introduce a combinatory feature selection strategy that integrates differential gene expression analysis with the Gram-Schmidt process to identify prognostic genes that are both statistically significant and highly informative for predicting tumour survival outcomes. Empirical application to leukemia and ovarian cancer survival data through-within- and cross-study validations shows that the feature space can be largely reduced while achieving improved testing performances.