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Modeling gene expression measurement error: a quasi-likelihood approach

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Modeling gene expression measurement error: a quasi-likelihood approach (English)

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author name string

Korbinian Strimmer

series ordinal

1

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language of work or name

English

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publication date

20 March 2003

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published in

BMC Bioinformatics

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volume

4

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issue

1

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page(s)

10

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cites work

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Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection

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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

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A variance-stabilizing transformation for gene-expression microarray data

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Model-based clustering and data transformations for gene expression data

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Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data

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On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data

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An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression

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A model for measurement error for gene expression arrays

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Statistical design and the analysis of gene expression microarray data

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Ratio-based decisions and the quantitative analysis of cDNA microarray images

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based on heuristic

inferred from PubMed ID database lookup

Identifiers

DOI

10.1186/1471-2105-4-10

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DBLP publication ID

journals/bmcbi/Strimmer03

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stated in

DBLP Dataset 2021-01-02

retrieved

28 January 2021

Dimensions Publication ID

1040110690

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PMC publication ID

153502

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PubMed publication ID

12659637

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ResearchGate publication ID

10835354

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(Q24792117)

Modeling gene expression measurement error: a quasi-likelihood approach

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