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Shrinkage Approach for Gene Expression Data Analysis
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SYSNO ASEP 0427425 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Ostatní články Title Shrinkage Approach for Gene Expression Data Analysis Author(s) Haman, Jiří (UIVT-O)
Valenta, Zdeněk (UIVT-O) RID, SAI, ORCIDSource Title European Journal for Biomedical Informatics. - : Pulsus Group - ISSN 1801-5603
Roč. 9, č. 3 (2013), s. 2-8Number of pages 7 s. Language eng - English Country CZ - Czech Republic Keywords microarray technology ; high dimensional data ; mean squared error ; James-Stein shrinkage estimator ; mutual information Subject RIV IN - Informatics, Computer Science Institutional support UIVT-O - RVO:67985807 DOI 10.24105/ejbi.2013.09.3.2 Annotation Background: Microarray technologies are used to measure the simultaneous expression of a certain set of thousands of genes based on ribonucleic acid (RNA) obtained from a biological sample. We are interested in several statistical analyses such as 1) finding differentially expressed genes between or among several experimental groups, 2) finding a small number of genes allowing for the correct classification of a sample in a certain group, and 3) finding relations among genes. Objectives: Gene expression data are high dimensional, and this fact complicates their analysis because we are able to perform only a few samples (e.g. the peripheral blood from a limited number of patients) for a certain set of thousands of genes. The main purpose of this paper is to present the shrinkage estimator and show its application in different statistical analyses. Methods: The shrinkage approach relates to the shift of a certain value of a classic estimator towards a certain value of a specified target estimator. More precisely, the shrinkage estimator is the weighted average of the classic estimator and the target estimator. Results: The benefit of the shrinkage estimator is that it improves the mean squared error (MSE) as compared to a classic estimator. The MSE combines the measure of an estimator’s bias away from its true unknown value and the measure of the estimator’s variability. The shrinkage estimator is a biased estimator but has a lower variability. Conclusions: The shrinkage estimator can be considered as a promising estimator for analyzing high dimensional gene expression data. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2015
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