Sentence examples for microarray data where from inspiring English sources

Effective analysis of microarray data, where the number of measured variables is orders of magnitude greater than the number of cases, requires specialized statistical methods which have recently been developed.

This is consistent with the microarray data, where production of mRNA for Nurr1 was induced only in the WT cell line, and production of mRNA for Nur77 was induced most drastically in the WT cell line, and less so in the PDGFRβ null and PDGFRα null cell lines (Figure 2).

It was shown that the SVD provides a mathematical framework for the modeling of DNA microarray data, where the mathematical variables and operations represent biological reality [32]: The variables, significant patterns uncovered in the data, correlate with activities of cellular elements, such as regulators or transcription factors.

This study deals with reverse engineering of gene regulatory networks from DNA microarray data, where no gold standard method exists.

This is also confirmed by the microarray data where the hypergeometric test displays apoptosis, tight junction and adherent junction pathways.

ArrayExpress is a repository of microarray data where files are both uploaded directly by researchers and imported from GEO.

The highly pleiotropic nature of Grr1 is revealed in the DNA-microarray data, where 24 out of 38 evaluated genes are found to be differentially expressed (in the Boolean sense).

Other less rigid classification methods were developed, especially for microarray data analysis where we are usually dealing with small number of measurements.

One should not confuse the SNP clusters here with the clusters obtained from the traditional gene expression microarray data analysis, where co-expressed genes in a cluster often have similar functions.

That argument is especially true in the setting of microarray data analysis, where one is more interested in studying as many relevant genes as possible, and less interested in sacrificing possible targets for the concern of making one mistake [ 6].

We could explain this behavior by some summation of single effect treatments, exactly the same as for the morphological transformations data of the 2 cellular lines after these treatments, in agreement with the microarray data analysis where the affected genes are fewer in number than the genes involved in single treatments.