Hybridization Dynamics Compensation in Microarray Experiments

2011 ◽  
pp. 255-261
Author(s):  
Raul Malutan ◽  
Pedro Gómez Vilda ◽  
Ioana Berindan Neagoe ◽  
Monica Borda
Keyword(s):  
Microarray Experiments

scholarly journals Answer to the comments of K. Dobbin, J. Shih and R. Simon on the paper 'Evaluation of the gene-specific dye-bias in cDNA microarray experiments'

2005 ◽  
Vol 21 (14) ◽  
pp. 3065-3065 ◽  
Author(s):  
M.-L. Martin-Magniette ◽  
J. Aubert ◽  
E. Cabannes ◽  
J.-J. Daudin
Keyword(s):  
Cdna Microarray ◽  
Microarray Experiments

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

scholarly journals An a posteriori strategy for enhancing gene discovery in anonymous cDNA microarray experiments

2004 ◽  
Vol 20 (11) ◽  
pp. 1721-1727 ◽  
Author(s):  
R. S. Anderssen ◽  
Y. Wu ◽  
R. Dolferus ◽  
I. Saunders
Keyword(s):  
Cdna Microarray ◽  
Gene Discovery ◽  
A Posteriori ◽  
Microarray Experiments

scholarly journals Statistical designs for two-color microarray experiments involving technical replication

2006 ◽  
Vol 51 (3) ◽  
pp. 2078-2090 ◽  
Author(s):  
Shin-Fu Tsai ◽  
Chen-Tuo Liao ◽  
Feng-Shun Chai
Keyword(s):  
Microarray Experiments ◽  
Statistical Designs ◽  
Technical Replication

Microarray Experiments on Risk Analysis Using R

2015 ◽  
pp. 147-158
Author(s):  
Teresa A. Oliveira ◽  
Amílcar Oliveira ◽  
Andreia A. Monteiro
Keyword(s):  
Risk Analysis ◽  
Microarray Experiments

Heterologous microarray experiments used to identify the early gene response to heat stress in a coral reef fish

2007 ◽  
Vol 16 (8) ◽  
pp. 1749-1763 ◽  
Author(s):  
KARIN S. KASSAHN ◽  
M. JULIAN CALEY ◽  
ALISTER C. WARD ◽  
ASHLEY R. CONNOLLY ◽  
GLENN STONE ◽  
...  
Keyword(s):  
Heat Stress ◽  
Coral Reef ◽  
Reef Fish ◽  
Coral Reef Fish ◽  
Early Gene ◽  
Microarray Experiments ◽  
Gene Response ◽  
Heterologous Microarray
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