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Statements

Subject Item

n2:RIV%2F00216208%3A11320%2F13%3A10159485%21RIV14-MSM-11320___

rdf:type

n13:Vysledek skos:Concept

rdfs:seeAlso

http://link.springer.com/book/10.1007%2F978-1-60327-337-4

dcterms:description

Microarrays for simultaneous measurement of redundancy of RNA species are widely used for fundamental biology research. They are also tested for their use in personalized medicine in disease diagnosis and prognosis. From the point of mathematical statistics, the invention of microarray technology in the mid-1990s allowed the simultaneous monitoring of the expression levels of thousands of genes. Microarrays for simultaneous measurement of redundancy of RNA species are used in fundamental biology as well as in medical research. Microarray may be considered as an observation of very high dimensionality equal to the number of expression levels. There arise some needs to develop new statistical methods to handle the data of such large dimensionality, especially connected to the fact of small number of observations (which is the number of arrays). Because of the small number of observations the standard asymptotic methods of multivariate statistical analysis appear to be inapplicable. The aim of the book is to familiarize the readers with statistical methods used nowadays in microarray analysis. It is addressed to everybody who is involved or is planning to be involved in statistical data analysis of microarrays, mostly to statisticians but also to biological researchers. Microarrays for simultaneous measurement of redundancy of RNA species are widely used for fundamental biology research. They are also tested for their use in personalized medicine in disease diagnosis and prognosis. From the point of mathematical statistics, the invention of microarray technology in the mid-1990s allowed the simultaneous monitoring of the expression levels of thousands of genes. Microarrays for simultaneous measurement of redundancy of RNA species are used in fundamental biology as well as in medical research. Microarray may be considered as an observation of very high dimensionality equal to the number of expression levels. There arise some needs to develop new statistical methods to handle the data of such large dimensionality, especially connected to the fact of small number of observations (which is the number of arrays). Because of the small number of observations the standard asymptotic methods of multivariate statistical analysis appear to be inapplicable. The aim of the book is to familiarize the readers with statistical methods used nowadays in microarray analysis. It is addressed to everybody who is involved or is planning to be involved in statistical data analysis of microarrays, mostly to statisticians but also to biological researchers.

dcterms:title

Statistical Methods for Microarray Data Analysis Statistical Methods for Microarray Data Analysis

skos:prefLabel

Statistical Methods for Microarray Data Analysis Statistical Methods for Microarray Data Analysis

skos:notation

RIV/00216208:11320/13:10159485!RIV14-MSM-11320___

n13:predkladatel

n16:orjk%3A11320

n4:aktivita

n17:I

n4:aktivity

I

n4:dodaniDat

n12:2014

n4:domaciTvurceVysledku

n5:1682458

n4:druhVysledku

n11:O

n4:duvernostUdaju

n15:S

n4:entitaPredkladatele

n18:predkladatel

n4:idSjednocenehoVysledku

107777

n4:idVysledku

RIV/00216208:11320/13:10159485

n4:jazykVysledku

n14:eng

n4:klicovaSlova

multiple testing procedures.; statistical test; data mining; gene expressions; Microarrays

n4:klicoveSlovo

n10:statistical%20test n10:gene%20expressions n10:data%20mining n10:multiple%20testing%20procedures. n10:Microarrays

n4:kontrolniKodProRIV

[E0B74BEA1BC1]

n4:obor

n9:BB

n4:pocetDomacichTvurcuVysledku

1

n4:pocetTvurcuVysledku

22

n4:rokUplatneniVysledku

n12:2013

n4:tvurceVysledku

Yakovlev, Andrei Klebanov, Lev Galle, Daniel

n19:doi

10.1007/978-1-60327-337-4

n7:organizacniJednotka

11320