A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals
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 pred...
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Published in | Advances in Bioinformatics Vol. 2009; no. 2009; pp. 1 - 7 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Cairo, Egypt
Hindawi Limiteds
01.01.2009
Hindawi Puplishing Corporation Hindawi Publishing Corporation Hindawi Limited |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Recommended by Paul Pavlidis |
ISSN: | 1687-8027 1687-8035 |
DOI: | 10.1155/2009/480486 |