Sentence examples for microarray datasets due from inspiring English sources

This uncovers subtle gene expression similarities in three otherwise disparate microarray datasets due to a shared strain background.

Ideally, the same pair of genes should have similar correlation across microarray datasets, but, due to platform differences and normalisation, these can vary.

The differences observed between compounds in the global transcriptional response (i.e. microarray dataset) are likely due to a combination of two factors: 1) authentic heterogeneity in the pharmacodynamic activities of deltamethrin and permethrin on gene transcription and 2) a slight offset in the time course of qualitatively similar responses across compounds.

Microarray datasets are excellent meta-analysis candidates due to the high use and deposit in publically accessible data banks, complete with information on experimental conduct.

However, it is worthwhile noting that integrating microarray datasets from different studies is not so simple due to the different microarray platforms across different datasets, different experimental protocols, and different preprocessing methods.

Confidence in differential expression measures can then be improved by utilising a pooled estimate of sample variance with empirical Bayes due to the parallel nature of microarray datasets [ 26].

Contact: [email protected]; [email protected] The analysis of gene coexpression patterns has been of great interest in recent years due to the widespread availability of microarray datasets measuring thousands of genes.

Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms.

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

Due to its extensive use, thousands of gene expression microarray datasets have been deposited to public databases making these repositories valuable data sources.

Okoniewski and Miller (BMC Bioinformatics 2006 7: 276) published an analysis of widely used microarray datasets and conclude that oligonucleotide microarrays are prone to false positives correlations due to multiple targeting.