We used a two-class paired significant analysis of microarray analysis to determine differential gene expression before and after treatment.
To overcome this limitation, Samson and her colleagues used microarray analysis to discover if looking at many genes in parallel could improve the prediction.
When using microarray analysis to determine gene dependence, one of the goals is to identify differentially expressed genes.
We have used subtractive cDNA libraries and microarray analysis to identify the gene expression profile that regulates tolerance to hypoxia.
Intestinal epithelial cells, like Caco-2, are commonly used to study the interaction between food, other luminal factors and the host, often supported by microarray analysis to study the changes in gene expression as a result of the exposure.
We used microarray analysis to identify new target genes in brain.
Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients.
Here, we used a zebrafish microarray analysis to identify heart-specific transcriptomic changes during early development that might underlie cardiotoxicity of BaP + FL.
To begin to address the molecular mechanisms involved, we used microarray analysis to identify potential down-stream targets of Neurog1 expressed at sequential stages of neuronal differentiation.
Dejima et al. used microarray analysis to examine plasma from 3 NSCLC patients with recurrence after surgery, 3 patients without recurrence and 3 healthy volunteers.
Chen et al. [22] used transcription factor microarray analysis to demonstrate that at least 30 transcription factors in Arabidopsis were induced by abiotic stress.
