Sentence examples for microarray data measured from inspiring English sources

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.

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.

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].

The microarray data measures the expressions of tens of thousands of genes, producing a feature vector that is high in dimensionality and that contains much irrelevant information.

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.

Internal cross-validation for genes would increase the complexity by a factor of G (total number of genes) which could be a substantial increase for microarray expression data measuring thousands of genes.

Since the microarray data was measured by the ratio of the hybridization signal for each gene, it could vary by factors of 2 or greater.