Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data.
about
Diurnally entrained anticipatory behavior in archaeaLearning a prior on regulatory potential from eQTL dataOptimized LOWESS normalization parameter selection for DNA microarray dataModeling gene expression measurement error: a quasi-likelihood approachWithin the fold: assessing differential expression measures and reproducibility in microarray assays.How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach.Incorporation of gene-specific variability improves expression analysis using high-density DNA microarraysClustering gene-expression data with repeated measurementsTests for finding complex patterns of differential expression in cancers: towards individualized medicineModel-based cluster analysis of microarray gene-expression dataBayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatmentsMultifactorial experimental design and the transitivity of ratios with spotted DNA microarraysResolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarraysA simple method for statistical analysis of intensity differences in microarray-derived gene expression dataEffect of sample size and P-value filtering techniques on the detection of transcriptional changes induced in rat neuroblastoma (NG108) cells by mefloquineA power law global error model for the identification of differentially expressed genes in microarray dataThe effects of normalization on the correlation structure of microarray dataDye bias correction in dual-labeled cDNA microarray gene expression measurements.Gene expression data classification with Kernel principal component analysis.Validation and refinement of gene-regulatory pathways on a network of physical interactionsAn approach for clustering gene expression data with error information.OpWise: operons aid the identification of differentially expressed genes in bacterial microarray experimentsNUP-1 Is a large coiled-coil nucleoskeletal protein in trypanosomes with lamin-like functionsA role for programmed cell death in the microbial loopA systems approach to delineate functions of paralogous transcription factors: role of the Yap family in the DNA damage response.Environment-responsive transcription factors bind subtelomeric elements and regulate gene silencing.Identification of TFB5, a new component of general transcription and DNA repair factor IIH.Transcriptional responses to fatty acid are coordinated by combinatorial controlTranscriptome profiling to identify genes involved in peroxisome assembly and functionSerological profiling of a Candida albicans protein microarray reveals permanent host-pathogen interplay and stage-specific responses during candidemiaSystems biology of innate immunityA distinct lineage of CD4 T cells regulates tissue inflammation by producing interleukin 17The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo.Bivariate microarray analysis: statistical interpretation of two-channel functional genomics data.Challenges for MicroRNA Microarray Data Analysis.Molecular mechanisms of system responses to novel stimuli are predictable from public dataAnalyzing array data using supervised methods.Genes selection comparative study in microarray data analysisTesting for differentially expressed genes with microarray data.Mining microarray data to identify transcription factors expressed in naïve resting but not activated T lymphocytes.
P2860
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P2860
Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data.
description
2000 nî lūn-bûn
@nan
2000 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2000 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2000年の論文
@ja
2000年論文
@yue
2000年論文
@zh-hant
2000年論文
@zh-hk
2000年論文
@zh-mo
2000年論文
@zh-tw
2000年论文
@wuu
name
Testing for differentially-exp ...... d analysis of microarray data.
@ast
Testing for differentially-exp ...... d analysis of microarray data.
@en
type
label
Testing for differentially-exp ...... d analysis of microarray data.
@ast
Testing for differentially-exp ...... d analysis of microarray data.
@en
prefLabel
Testing for differentially-exp ...... d analysis of microarray data.
@ast
Testing for differentially-exp ...... d analysis of microarray data.
@en
P2093
P1476
Testing for differentially-exp ...... d analysis of microarray data.
@en
P2093
P304
P356
10.1089/10665270050514945
P577
2000-01-01T00:00:00Z