Gene selection for predicting survival outcomes of cancer patients in microarray studies

Qihua Tan; Mads Thomassen; Kirsten Marie Jochumsen; Ole Mogensen; Kaare Christensen; Torben Kruse

doi:10.1007/978-1-4020-8741-7_73

Gene selection for predicting survival outcomes of cancer patients in microarray studies

Qihua Tan^*, Mads Thomassen, Kirsten Marie Jochumsen, Ole Mogensen, Kaare Christensen, Torben Kruse

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Abstract

In this paper, we introduce a multivariate approach for selecting genes for predicting survival outcomes of cancer patients in gene expression microarray studies. Combined with survival analysis for gene filtering, the method makes full use of individual’s survival information (both censored and uncensored) in selecting informative genes for survival outcome prediction. Application of our method to published data on epithelial ovarian cancer has identified genes that discriminate unfavorable and favorable outcomes with high significance ( χ2 = 21.933, p = 3e - 06 ). The method can also be generalized to categorical variables for selecting gene expression signatures for predicting tumor metastasis or tumor subtypes.

Original language	English
Title of host publication	Advances in Computer and Information Sciences and Engineering
Editors	Tarek Sobh
Publisher	Springer Science+Business Media
Publication date	2008
Pages	405-409
ISBN (Print)	978-1-4020-8740-0
DOIs	https://doi.org/10.1007/978-1-4020-8741-7_73
Publication status	Published - 2008

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10.1007/978-1-4020-8741-7_73

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title = "Gene selection for predicting survival outcomes of cancer patients in microarray studies",

abstract = "In this paper, we introduce a multivariate approach for selecting genes for predicting survival outcomes of cancer patients in gene expression microarray studies. Combined with survival analysis for gene filtering, the method makes full use of individual{\textquoteright}s survival information (both censored and uncensored) in selecting informative genes for survival outcome prediction. Application of our method to published data on epithelial ovarian cancer has identified genes that discriminate unfavorable and favorable outcomes with high significance ( χ2 = 21.933, p = 3e - 06 ). The method can also be generalized to categorical variables for selecting gene expression signatures for predicting tumor metastasis or tumor subtypes.",

author = "Qihua Tan and Mads Thomassen and Jochumsen, {Kirsten Marie} and Ole Mogensen and Kaare Christensen and Torben Kruse",

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Gene selection for predicting survival outcomes of cancer patients in microarray studies. / Tan, Qihua ; Thomassen, Mads ; Jochumsen, Kirsten Marie et al.
Advances in Computer and Information Sciences and Engineering. ed. / Tarek Sobh. Springer Science+Business Media, 2008. p. 405-409.

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

TY - CHAP

T1 - Gene selection for predicting survival outcomes of cancer patients in microarray studies

AU - Tan, Qihua

AU - Thomassen, Mads

AU - Jochumsen, Kirsten Marie

AU - Mogensen, Ole

AU - Christensen, Kaare

AU - Kruse, Torben

PY - 2008

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AB - In this paper, we introduce a multivariate approach for selecting genes for predicting survival outcomes of cancer patients in gene expression microarray studies. Combined with survival analysis for gene filtering, the method makes full use of individual’s survival information (both censored and uncensored) in selecting informative genes for survival outcome prediction. Application of our method to published data on epithelial ovarian cancer has identified genes that discriminate unfavorable and favorable outcomes with high significance ( χ2 = 21.933, p = 3e - 06 ). The method can also be generalized to categorical variables for selecting gene expression signatures for predicting tumor metastasis or tumor subtypes.

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DO - 10.1007/978-1-4020-8741-7_73

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BT - Advances in Computer and Information Sciences and Engineering

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Gene selection for predicting survival outcomes of cancer patients in microarray studies

Abstract

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