Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data.
about
Review of the literature examining the correlation among DNA microarray technologiesExploiting dependencies of pairwise comparison outcomes to predict patterns of gene responseA robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinomaDifferential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosisA network-based method to evaluate quality of reproducibility of differential expression in cancer genomics studiesThe effect of listening to music on human transcriptomeIntra- and inter-individual variance of gene expression in clinical studiesFunctional cohesion of gene sets determined by latent semantic indexing of PubMed abstractsIdentification and optimization of classifier genes from multi-class earthworm microarray datasetEvaluating microarray-based classifiers: an overviewThe effect of music performance on the transcriptome of professional musicians.Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollutionGenome-wide analysis of plant nat-siRNAs reveals insights into their distribution, biogenesis and function.False discovery rate control in two-stage designs.Literature aided determination of data quality and statistical significance threshold for gene expression studies.Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the ChIP-chip data.Combining multiple hypothesis testing and affinity propagation clustering leads to accurate, robust and sample size independent classification on gene expression data.An integrated approach for identifying wrongly labelled samples when performing classification in microarray dataAnalysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation.Identification of significant features in DNA microarray data.Genes selection comparative study in microarray data analysisEmpirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data.Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions.Profiling early lung immune responses in the mouse model of tuberculosis.Evaluating methods for ranking differentially expressed genes applied to microArray quality control data.Importance of data structure in comparing two dimension reduction methods for classification of microarray gene expression data.Pigeons: A Novel GUI Software for Analysing and Parsing High Density Heterologous Oligonucleotide Microarray Probe Level DataA weighted average difference method for detecting differentially expressed genes from microarray data.New pleiotropic effects of eliminating a rare tRNA from Streptomyces coelicolor, revealed by combined proteomic and transcriptomic analysis of liquid culturesA unified framework for finding differentially expressed genes from microarray experimentsA Population Proportion approach for ranking differentially expressed genes.The rules of gene expression in plants: organ identity and gene body methylation are key factors for regulation of gene expression in Arabidopsis thalianaNot proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experimentsComparison of small n statistical tests of differential expression applied to microarrays.A voting approach to identify a small number of highly predictive genes using multiple classifiers.Data perturbation independent diagnosis and validation of breast cancer subtypes using clustering and patterns.A gene signature for post-infectious chronic fatigue syndrome.Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray dataValidation of differential gene expression algorithms: application comparing fold-change estimation to hypothesis testingA multi-strategy approach to informative gene identification from gene expression data.
P2860
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P2860
Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data.
description
2006 nî lūn-bûn
@nan
2006 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2006 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2006年の論文
@ja
2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
name
Comparison and evaluation of m ...... ne lists from microarray data.
@ast
Comparison and evaluation of m ...... ne lists from microarray data.
@en
type
label
Comparison and evaluation of m ...... ne lists from microarray data.
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Comparison and evaluation of m ...... ne lists from microarray data.
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prefLabel
Comparison and evaluation of m ...... ne lists from microarray data.
@ast
Comparison and evaluation of m ...... ne lists from microarray data.
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P2860
P50
P356
P1433
P1476
Comparison and evaluation of m ...... ene lists from microarray data
@en
P2860
P2888
P356
10.1186/1471-2105-7-359
P577
2006-07-26T00:00:00Z
P5875
P6179
1024464977