Sentence examples for microarray data need from inspiring English sources

In general, real-time RT-PCR is more quantitative, accurate and often microarray data need validation by RT-PCR.

To obtain significant results, microarray data need to undergo statistical processing to differentiate between signal changes caused by direct experimental factors and arising from the indirect experimental factors such as specific methods used, as well as from inaccuracies of the measurements.

In general, cDNA microarray expression data need to be validated to ensure that the correct gene expression has been measured.

Given the inconsistency of results across similar studies, methods that identify robust biomarkers from microarray data are needed to relay true biological information.

Therefore, an automated and objective computation of the correlation between labels and microarray data is needed to identify correlated clinical parameters.

We also argue that the potential of applying fuzzy-based techniques to microarray data analysis need to be further explored.

In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns.

Microarray data of course need to be confirmed in subsequent studies, e.g. by targeted gene deletion or RNAi experiments, to address the function of individual genes to attenuate fungicide impact and the mechanisms by which this may be achieved.

In the present paper, we discuss one feature of microarray data the investigators need to be aware of when embarking on a study of putative associations between elements of networks and pathways.

The main type of experimental data needed is transcription (microarray) and methylation (methylation chip) datasets from different cell types subjected to various types of stress.

For instance, the diversity of microarray platforms used for gene expression analysis and the variability in microarray data emphasise the need for quality control.