We use hundreds of microarray experiments to infer the nature of the regulatory interactions among genes, and implement these data into the Boolean models of the genetic networks.
Expression responses of genes to cytokine treatments were evaluated using a panel of microarray experiments described previously [60].
Using the results of microarray experiments that studied gene expression in cold-treated peach mesocarp tissue, 63 cold-responsive genes were located on the fruit quality gene map, allowing the detection of co-locations of these CI resistance CGs with QTLs for CI symptoms.
Because qRT-PCR is used to validate the results of microarray experiments, we used TaqMan miRNA arrays to explore the correlation of arrays with qRT-PCR.
A separate template was used to collect details of microarray experiments, including sample collection procedures, cell separation techniques, target cell types, methods used to extract ribonucleic acids, cDNA synthesis and hybirdization, microarray platforms used, number of probe set on arrays, microarray data processing and normalization methods.
In a series of case studies, we showed how this strategy can be used to scan thousands of microarray experiments in the public domain against brown fat-related gene sets to glean insight into adipose tissue biology.
Some of these modules were further investigated using the Genevestigator compendium of microarray experiments.
These test statistics have been used since the beginning of microarray experiments, although their performances have not been sufficiently evaluated.
An Arabidopsis full genome analysis using a large collection of microarray experiments was performed by Ma and colleagues [ 21].
We first tested the approach by using a published dataset of microarray experiments in yeast [ 13], and then applied it to the analysis of human prostate cancer samples [ 14].
