Sentence examples for multiple independent microarray from inspiring English sources

We present a new approach for combining data from multiple independent microarray studies.

Integrated analysis of multiple independent microarray datasets has drawn noteworthy interests recently [ 20- 22].

Experiments showed that coexpression patterns mined from multiple independent microarray datasets are more likely to be functionally relevant and thus improve gene function predictions [ 1].

In a meta-analysis of multiple, independent microarray datasets, Notch-1, survivin, and keratin-5 selectively co-associated with ER-negative versus ER-positive breast cancer patients.

Rhodes et al. [ 6] developed a statistical approach to compare multiple independent microarray data sets and applied it to identify sets of consistently differentially expressed genes across four independent prostate cancer gene data sets.

To discover novel oncogenes associated with breast cancer progression, we used an informatics approach [ 1] and mined multiple independent microarray datasets of breast tumor profiles for genes having significant and reproducible associations with distant metastasis-free survival (DMFS).

In summary, by integrating multiple independent DNA microarray studies of differentially expressed genes in human uterine fibroids a group of thirty-eight genes were identified as consistently changed in the tumor versus surrounding normal myometrium.

Data integration techniques, so-called meta-analyses, aim to combine the data available and integrate information from multiple independent but related microarray studies to identify significant genes [reviewed by Feichtinger et al. (5)].

Although this would predict strong binding enrichment on A-down genes, this may be difficult to observe because the A-down gene set is small (101 genes), and because the analysis demands intersections between multiple sets of independent microarray and ChIP data, each adding some noise to the analysis.

RNA from multiple independent infections was collected for microarray analysis using the Affymetrix Human Genome U133 microarray platform.

Results: We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes.