Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset
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Dysregulation of mitochondrial dynamics and the muscle transcriptome in ICU patients suffering from sepsis induced multiple organ failureNetwork-based analysis of comorbidities risk during an infection: SARS and HIV case studiesReliability and reproducibility issues in DNA microarray measurementsTIF1gamma controls erythroid cell fate by regulating transcription elongationDisruption of the Fbxw8 gene results in pre- and postnatal growth retardation in miceEstimation and correction of non-specific binding in a large-scale spike-in experimentGlobal gene expression of Prochlorococcus ecotypes in response to changes in nitrogen availabilityCorrelation test to assess low-level processing of high-density oligonucleotide microarray dataIdentifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approachEvaluation of methods for oligonucleotide array data via quantitative real-time PCRProposed methods for testing and selecting the ERCC external RNA controlsUtilization of two sample t-test statistics from redundant probe sets to evaluate different probe set algorithms in GeneChip studies.DNA methylation data analysis and its application to cancer researchMolecular alterations in areas generating fast ripples in an animal model of temporal lobe epilepsyEpoxyeicosatrienoic acids enhance embryonic haematopoiesis and adult marrow engraftment.A systems level, functional genomics analysis of chronic epilepsyHigh-resolution DNA-binding specificity analysis of yeast transcription factors.Effects of oestrogen on trigeminal ganglia in culture: implications for hormonal effects on migraineIdentification of receptor-tyrosine-kinase-signaling target genes reveals receptor-specific activities and pathway branchpoints during Drosophila developmentPutative null distributions corresponding to tests of differential expression in the Golden Spike dataset are intensity dependentIntensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experimentsPrior robust empirical Bayes inference for large-scale data by conditioning on rank with application to microarray data.Cell cycle-dependent nucleosome occupancy at cohesin binding sites in yeast chromosomes.Gene selection with multiple ordering criteria.Correction of scaling mismatches in oligonucleotide microarray data.Variation in fiberoptic bead-based oligonucleotide microarrays: dispersion characteristics among hybridization and biological replicate samples.BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips.Preferred analysis methods for Affymetrix GeneChips. II. An expanded, balanced, wholly-defined spike-in dataset.Gene expression profile of rat left ventricles reveals persisting changes following chronic mild exercise protocol: implications for cardioprotection.Gene expression profiles in a rabbit model of systemic lupus erythematosus autoantibody productionBiological assessment of robust noise models in microarray data analysis.Combining multiple hypothesis testing and affinity propagation clustering leads to accurate, robust and sample size independent classification on gene expression data.Iterative rank-order normalization of gene expression microarray data.Genes selection comparative study in microarray data analysisNormalyzer: a tool for rapid evaluation of normalization methods for omics data setsVariation-preserving normalization unveils blind spots in gene expression profiling.A close examination of double filtering with fold change and T test in microarray analysis.How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different resultsComparison and evaluation of methods for generating differentially expressed gene lists from microarray data.A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database.
P2860
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P2860
Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset
description
2005 nî lūn-bûn
@nan
2005 թուականին հրատարակուած գիտական յօդուած
@hyw
2005 թվականին հրատարակված գիտական հոդված
@hy
2005年の論文
@ja
2005年論文
@yue
2005年論文
@zh-hant
2005年論文
@zh-hk
2005年論文
@zh-mo
2005年論文
@zh-tw
2005年论文
@wuu
name
Preferred analysis methods for ...... wholly defined control dataset
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Preferred analysis methods for ...... wholly defined control dataset
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Preferred analysis methods for ...... wholly defined control dataset
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Preferred analysis methods for ...... wholly defined control dataset
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label
Preferred analysis methods for ...... wholly defined control dataset
@ast
Preferred analysis methods for ...... wholly defined control dataset
@en
Preferred analysis methods for ...... wholly defined control dataset
@en-gb
Preferred analysis methods for ...... wholly defined control dataset
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prefLabel
Preferred analysis methods for ...... wholly defined control dataset
@ast
Preferred analysis methods for ...... wholly defined control dataset
@en
Preferred analysis methods for ...... wholly defined control dataset
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Preferred analysis methods for ...... wholly defined control dataset
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P2093
P2860
P3181
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Preferred analysis methods for ...... wholly defined control dataset
@en
P2093
Alan M Michelson
George M Church
Marc S Halfon
Sung E Choe
P2860
P2888
P3181
P356
10.1186/GB-2005-6-2-R16
P407
P50
P577
2005-01-28T00:00:00Z
P5875
P6179
1028489174