This HTML5 document contains 41 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n15http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n13http://linked.opendata.cz/ontology/domain/vavai/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
rdfshttp://www.w3.org/2000/01/rdf-schema#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n14http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F67985807%3A_____%2F13%3A00427425%21RIV15-AV0-67985807/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n8http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n17http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n12http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n16http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n6http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F13%3A00427425%21RIV15-AV0-67985807
rdf:type
skos:Concept n13:Vysledek
rdfs:seeAlso
http://www.ejbi.org/img/ejbi/2013/3/Haman_en.pdf
dcterms:description
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. 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.
dcterms:title
Shrinkage Approach for Gene Expression Data Analysis Shrinkage Approach for Gene Expression Data Analysis
skos:prefLabel
Shrinkage Approach for Gene Expression Data Analysis Shrinkage Approach for Gene Expression Data Analysis
skos:notation
RIV/67985807:_____/13:00427425!RIV15-AV0-67985807
n3:aktivita
n17:I n17:S
n3:aktivity
I, S
n3:cisloPeriodika
3
n3:dodaniDat
n6:2015
n3:domaciTvurceVysledku
n15:6423205 n15:1445804
n3:druhVysledku
n16:J
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
104988
n3:idVysledku
RIV/67985807:_____/13:00427425
n3:jazykVysledku
n12:eng
n3:klicovaSlova
microarray technology; high dimensional data; mean squared error; James-Stein shrinkage estimator; mutual information
n3:klicoveSlovo
n4:microarray%20technology n4:high%20dimensional%20data n4:James-Stein%20shrinkage%20estimator n4:mutual%20information n4:mean%20squared%20error
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[3FCFC7BB549A]
n3:nazevZdroje
European Journal for Biomedical Informatics
n3:obor
n7:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n6:2013
n3:svazekPeriodika
9
n3:tvurceVysledku
Valenta, Zdeněk Haman, Jiří
s:issn
1801-5603
s:numberOfPages
7