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Current Awareness on Comparative and Functional GenomicsDesigning toxicogenomics studies that use DNA array technologyAn assessment of recently published gene expression data analyses: reporting experimental design and statistical factorsA simulation-approximation approach to sample size planning for high-dimensional classification studies.Selection of differentially expressed genes in microarray data analysis.A simple method for assessing sample sizes in microarray experiments.A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray dataGene expression profiling in the rhesus macaque: methodology, annotation and data interpretation.Power and sample size estimation in microarray studiesStatistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology.Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithmsSotos syndrome is associated with deregulation of the MAPK/ERK-signaling pathway.Genes dysregulated to different extent or oppositely in estrogen receptor-positive and estrogen receptor-negative breast cancers.Clinical uses of microarrays in cancer research.Power and sample size calculation for microarray studies.Use of genomic signatures in therapeutics development in oncology and other diseases.Machine learning-based receiver operating characteristic (ROC) curves for crisp and fuzzy classification of DNA microarrays in cancer researchSample size calculation in metabolic phenotyping studies.An interactive power analysis tool for microarray hypothesis testing and generation.Practical guidelines for assessing power and false discovery rate for a fixed sample size in microarray experimentsGene Selection using a High-Dimensional Regression Model with Microarrays in Cancer Prognostic Studies.Sample size calculation through the incorporation of heteroscedasticity and dependence for a penalized t-statistic in microarray experiments.Sample size calculations for controlling the distribution of false discovery proportion in microarray experiments.Sample size calculations based on ranking and selection in microarray experiments.Estimating effect sizes of differentially expressed genes for power and sample-size assessments in microarray experiments.Comments on probabilistic models behind the concept of false discovery rate.Biomarker classifiers for identifying susceptible subpopulations for treatment decisions.Sample sizes for a robust ranking and selection of genes in microarray experiments.cDNA microarray analysis of human keratinocytes cells of patients submitted to chemoradiotherapy and oral photobiomodulation therapy: pilot study.A two-stage binomial test approach of gene identification in oligonucleotide arrays.Sample Size Calculation for Controlling False Discovery Proportion
P2860
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P2860
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年学术文章
@wuu
2004年学术文章
@zh
2004年学术文章
@zh-cn
2004年学术文章
@zh-hans
2004年学术文章
@zh-my
2004年学术文章
@zh-sg
2004年學術文章
@yue
2004年學術文章
@zh-hant
name
Sample size for gene expression microarray experiments.
@en
Sample size for gene expression microarray experiments.
@nl
type
label
Sample size for gene expression microarray experiments.
@en
Sample size for gene expression microarray experiments.
@nl
prefLabel
Sample size for gene expression microarray experiments.
@en
Sample size for gene expression microarray experiments.
@nl
P2093
P356
P1433
P1476
Sample size for gene expression microarray experiments.
@en
P2093
Dung-Tsa Chen
James J Chen
Sue-Jane Wang
P304
P356
10.1093/BIOINFORMATICS/BTI162
P407
P50
P577
2004-11-25T00:00:00Z