ϑ is the parameter that defines the weighting factor of each objective function (ϑ = 0.5 for all simulations), is the expression profile under viral infection of all genes (Ng) of A. thaliana and λg ∈ (0, 1) is a parameter defined for each gene that differences those genes differentially expressed in the microarray data measured under viral conditions.
We apply this method to microarray data measuring gene expression changes in cell lines transfected with certain miRNAs or anti-miRNAs (miRNA-specific inhibitors).
Further evidence suggesting a link between chromosome replication and expression of nucleotide biosynthesis genes was obtained from microarray data measuring the induction of genes in the SOS regulon after exposure of cells to UV-irradiation [34].
We used microarray data measured in three biological replicates each (except for quiescent center which had two replicates) on the Affymetrix ATH1 GeneChip from the following seven tissues: lateral root cap and epidermis [13]; quiescent center and columella [14]; cortex, xylem and phloem [7].
The microarray data measured gene expression levels in four different mouse tissues: liver, brain, adipose and muscle.
In this article, we explore the relationship between histone deacetylation sites and gene expression patterns on the genome scale using different data sources, including microarray data measuring gene expression levels, microarray data measuring histone deacetylation sites, and information on regulatory targets of transcription factors.
Processed Arabidopsis and rice microarray data measured by the Affymetrix Arabidopsis ATH1 Genome Array (GEO platform GPL198) and GeneChip Rice Genome Array (GEO platform GPL2020), respectively, were obtained from a previous study (Wang Y, et al. 2011).
The microarray data measured previously for five liver samples of both groups at each of 5 time points [ 12] are analyzed by the standard statistical techniques and the network screening.
First, the regulatory networks are compiled by using the known binary relationships between the transcriptional factors and their regulated genes and the biological classification scheme, and second, the consistency of each regulatory network with the microarray data measured in GK rat is estimated to detect the active networks under the corresponding conditions.
Gene co-expression networks are constructed from microarray data that measures the transcriptional response of cells to changing conditions.
