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Bayesian classification for bivariate normal gene expression

Listed author(s):
  • Ramos, Sandra
  • Amaral Turkman, Antónia
  • Antunes, Marília
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    A Bayesian optimal screening method (BOSc) is proposed to classify an individual into one of two groups, based on the observation of pairs of covariates, namely the expression level of pairs of genes (previously selected by a specific method, among the thousands of genes present in the microarray) measured using DNA microarrays technology. The method is general and can be applied to any correlated pair of screening variables, either with a bivariate normal distribution or which can be transformed into a bivariate normal.1 Results on microarray data sets (Leukemia, Prostate and Breast) show that BOSc performance is competitive with, and in some cases significantly better than, quadratic and linear discriminant analyses and support vector machines classifiers. BOSc provides flexible parametric decision rules. Finally, the screening classifier allows the calculation of operating characteristics while addressing information about the prevalence of the disease or type of disease, which is an advantage over other classification methods.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 54 (2010)
    Issue (Month): 8 (August)
    Pages: 2012-2020

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    Handle: RePEc:eee:csdana:v:54:y:2010:i:8:p:2012-2020
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    1. Nguyen, Danh V. & Rocke, D.M.David M., 2004. "On partial least squares dimension reduction for microarray-based classification: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 407-425, June.
    2. Dai Jian J & Lieu Linh & Rocke David, 2006. "Dimension Reduction for Classification with Gene Expression Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-21, February.
    3. Shim, Jooyong & Sohn, Insuk & Kim, Sujong & Lee, Jae Won & Green, Paul E. & Hwang, Changha, 2009. "Selecting marker genes for cancer classification using supervised weighted kernel clustering and the support vector machine," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1736-1742, March.
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