Sentence examples for microarray datasets such from inspiring English sources

Recently, differential co-expression bicluster mining has been used to infer the reasonable patterns in two microarray datasets, such as, normal and cancer cells.

For unknown microarray datasets, such information is unavailable.

However, due to the small number of instances in gene microarray datasets, such an approach can lead to unreliable results.

However, direct combination of multiple microarray datasets such as stacking them into one is extremely difficult owing to the incompatibility of data generated from various microarray platforms and different versions within the same platform (Mah et al., 2004).

That hypothesis predicts that this cohort of genes should be enriched in ER+/ER− tumors, and our GSEA analysis of a large microarray dataset from such tumors is consistent with this prediction (p<0.001, q = 0.06).

Recent availability of comprehensive mouse cardiac hypertrophy microarray datasets, deposited in resources such as ArrayExpress [ 11] and Gene Expression Omnibus [ 12], makes it possible to investigate global molecular mechanisms of this phenotype.

Although successful in analyzing small datasets, the above mentioned correlation or distance measures will be less helpful for searching large datasets, such as microarray compendium data.

Feature selection plays an undeniably important role in classification problems involving high dimensional datasets such as microarray datasets.

Information about these datasets, such as microarray platforms, the number of samples available, etc, is listed in Table 2.

Genomic features currently must be manipulated with reference to the underlying genomic sequence, which can make working with post-genomic datasets, such as microarray results, overly complex.

GenomeGraphs (Durinck et al., 2009) is a R package, which allows the visualization of one genomic region with related datasets such as microarray data.