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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.<\/jats:p>","DOI":"10.1155\/2009\/480486","type":"journal-article","created":{"date-parts":[[2009,7,30]],"date-time":"2009-07-30T15:57:36Z","timestamp":1248969456000},"page":"1-7","source":"Crossref","is-referenced-by-count":1,"title":["A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals"],"prefix":"10.1155","volume":"2009","author":[{"given":"Qihua","family":"Tan","sequence":"first","affiliation":[{"name":"Epidemiology, Institute of Public Health, University of Southern Denmark, J. B. Winsl\u00f8ws Vej 9B, 5000 Odense C, Denmark"},{"name":"Department of Biochemistry, Pharmacology and Genetics (BFG), Odense University Hospital, Sdr. 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