scholarly journals VARAN: a web server for Variability Analysis of DNA microarray experiments

2004 ◽  
Vol 20 (10) ◽  
pp. 1641-1643 ◽  
Author(s):  
G. Golfier ◽  
M. T. Dang ◽  
L. Dauphinot ◽  
E. Graison ◽  
J. Rossier ◽  
...  
Keyword(s):  
Dna Microarray ◽  
Web Server ◽  
Microarray Experiments ◽  
Variability Analysis

QUALITY CONTROL AND REPRODUCIBILITY IN DNA MICROARRAY EXPERIMENTS

2009 ◽  
Author(s):  
ANDRÉ FUJITA ◽  
JOÃO R. SATO ◽  
FERNANDO H.L. DA SILVA ◽  
MARIA C. GALVÃO ◽  
MARI C. SOGAYAR ◽  
...  
Keyword(s):  
Quality Control ◽  
Dna Microarray ◽  
Microarray Experiments

Comparison of sample-labeling techniques in DNA microarray experiments

2004 ◽  
Vol 506 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Keith Baggerly ◽  
Rahul Mitra ◽  
Rachel Grier ◽  
Dina Medhane ◽  
Guillermina Lozano ◽  
...  
Keyword(s):  
Dna Microarray ◽  
Microarray Experiments

True and false discovery in DNA microarray experiments: Transcriptome changes in the hippocampus of presenilin 1 mutant mice

Methods ◽  
2005 ◽  
Vol 37 (3) ◽  
pp. 261-273 ◽  
Author(s):  
Travis Unger ◽  
Zeljka Korade ◽  
Orly Lazarov ◽  
David Terrano ◽  
Sangram S. Sisodia ◽  
...  
Keyword(s):  
Dna Microarray ◽  
Mutant Mice ◽  
Presenilin 1 ◽  
Microarray Experiments ◽  
False Discovery

scholarly journals AtCAST, a Tool for Exploring Gene Expression Similarities among DNA Microarray Experiments Using Networks

2010 ◽  
Vol 52 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Eriko Sasaki ◽  
Chitose Takahashi ◽  
Tadao Asami ◽  
Yukihisa Shimada
Keyword(s):  
Gene Expression ◽  
Dna Microarray ◽  
Microarray Experiments

scholarly journals Using Copula to Model Dependence When Testing Multiple Hypotheses in DNA Microarray Experiments: A Bayesian Approximation

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1514
Author(s):  
Elisa C. J. Maria ◽  
Isabel Salazar ◽  
Luis Sanz ◽  
Miguel A. Gómez-Villegas
Keyword(s):  
Dna Microarray ◽  
Simulated Data ◽  
Real Data ◽  
Copula Functions ◽  
Microarray Experiments ◽  
Multiple Hypotheses ◽  
Bayesian Approximation ◽  
The Right ◽  
Dependency Structures ◽  
Available Information

Many experiments require simultaneously testing many hypotheses. This is particularly relevant in the context of DNA microarray experiments, where it is common to analyze many genes to determine which of them are differentially expressed under two conditions. Another important problem in this context is how to model the dependence at the level of gene expression. In this paper, we propose a Bayesian procedure for simultaneously testing multiple hypotheses, modeling the dependence through copula functions, where all available information, both objective and subjective, can be used. The approach has the advantage that it can be used with different dependency structures. Simulated data analysis was performed to examine the performance of the proposed approach. The results show that our procedure captures the dependence appropriately classifying adequately a high percentage of true and false null hypotheses when choosing a prior distribution beta skewed to the right for the initial probability of each null hypothesis, resulting in a very powerful procedure. The procedure is also illustrated with real data.

scholarly journals PVS: a web server for protein sequence variability analysis tuned to facilitate conserved epitope discovery

2008 ◽  
Vol 36 (Web Server) ◽  
pp. W35-W41 ◽  
Author(s):  
M. Garcia-Boronat ◽  
C. M. Diez-Rivero ◽  
E. L. Reinherz ◽  
P. A. Reche
Keyword(s):  
Protein Sequence ◽  
Web Server ◽  
Sequence Variability ◽  
Variability Analysis ◽  
Epitope Discovery ◽  
Conserved Epitope

DNA Microarray Experiments: Biological and Technological Aspects

Biometrics ◽  
2002 ◽  
Vol 58 (4) ◽  
pp. 701-717 ◽  
Author(s):  
Danh V. Nguyen ◽  
A. Bulak Arpat ◽  
Naisyin Wang ◽  
Raymond J. Carroll
Keyword(s):  
Dna Microarray ◽  
Microarray Experiments
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