An Efficient Semiparametric Approach for Marker Gene Selection and Patient Classification

Jingjing Wu, Guoqiang Chen and Zeny Feng
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Jingjing Wu: Department of Mathematics and Statistics, University of Calgary, Canada
Guoqiang Chen: Enbridge, Canada
Zeny Feng: Department of Mathematics and Statistics, University of Guelph, Canada

Biostatistics and Biometrics Open Access Journal, 2017, vol. 1, issue 2, 40-49

Abstract: The advancement of microarray technology has greatly facilitated the research in gene expression based classification of patient samples. For example, in cancer research, microarray gene expression data has been used for cancer or tumor classification. When the study is only focusing on two classes, for example two different cancer types, we propose a two-sample semiparametric model to model the distributions of gene expression level for different classes. To estimate the parameters, we consider both maximum semiparametric likelihood estimate (MLE) and minimum Hellinger distance estimate (MHDE).

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:1:y:2017:i:2:p:40-49

DOI: 10.19080/BBOAJ.2017.01.555558

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