Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1471-2105
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data ; volume:9 ; number:1 ; day:06 ; month:6 ; year:2008 ; pages:1-16 ; date:12.2008
BMC bioinformatics ; 9, Heft 1 (06.6.2008), 1-16, 12.2008
- Creator
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Jonnalagadda, Sudhakar
- Contributor
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Srinivasan, Rajagopalan
SpringerLink (Online service)
- DOI
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10.1186/1471-2105-9-267
- URN
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urn:nbn:de:1111-201602175829
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:22 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Jonnalagadda, Sudhakar
- Srinivasan, Rajagopalan
- SpringerLink (Online service)
Other Objects (12)
Identification of differentially expressed genes in microarray data in a principal component space
Sample size for detecting differentially expressed genes in microarray experiments
Cross platform microarray analysis for robust identification of differentially expressed genes
A unified framework for finding differentially expressed genes from microarray experiments
Robust test method for time-course microarray experiments
Nonparametric modeling approach for discovering differentially expressed genes in replicated microarray experiments
Nonparametric Modeling Approach for Discovering Differentially Expressed Genes in Replicated Microarray Experiments
Density based pruning for identification of differentially expressed genes from microarray data
Determination of the differentially expressed genes in microarray experiments using local FDR
Microarray profile of differentially expressed genes in a monkey model of allergic asthma
Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset
Improving identification of differentially expressed genes in microarray studies using information from public databases
Identification of differentially expressed genes in microarray data in a principal component space
Sample size for detecting differentially expressed genes in microarray experiments
Cross platform microarray analysis for robust identification of differentially expressed genes
A unified framework for finding differentially expressed genes from microarray experiments
Robust test method for time-course microarray experiments
Nonparametric modeling approach for discovering differentially expressed genes in replicated microarray experiments
Nonparametric Modeling Approach for Discovering Differentially Expressed Genes in Replicated Microarray Experiments
Density based pruning for identification of differentially expressed genes from microarray data
Determination of the differentially expressed genes in microarray experiments using local FDR
Microarray profile of differentially expressed genes in a monkey model of allergic asthma
Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset
Improving identification of differentially expressed genes in microarray studies using information from public databases
Identification of differentially expressed genes in microarray data in a principal component space
Sample size for detecting differentially expressed genes in microarray experiments
Cross platform microarray analysis for robust identification of differentially expressed genes
A unified framework for finding differentially expressed genes from microarray experiments
Robust test method for time-course microarray experiments
Nonparametric modeling approach for discovering differentially expressed genes in replicated microarray experiments
Nonparametric Modeling Approach for Discovering Differentially Expressed Genes in Replicated Microarray Experiments
Density based pruning for identification of differentially expressed genes from microarray data
Determination of the differentially expressed genes in microarray experiments using local FDR
Microarray profile of differentially expressed genes in a monkey model of allergic asthma
Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset