Sentence examples for microarray experiment measures from inspiring English sources

The microarray experiment measures mRNA expression changes in liver cancer tissue and adjacent non-tumour tissue.

Another level of noise originates from the fact that each microarray experiment measures a single value for a gene that reflects its activity averaged across many biological processes.

A microarray experiment measures genome-wide gene expression levels of mRNA in a cell or a tissue sample under a particular condition.

The most important feature for identifying known targets of Swi6 is a microarray experiment measuring expression of genes in an Mbp1 deletion mutant.

In brief, this microarray experiment measured changes in gene expression patterns in three different types of cassava roots, including fibrous, developing storage and mature storage roots which are believed to possess distinct predominant metabolism, especially those related to starch biosynthesis.

As microarray experiments measure the steady-state levels of mRNA and Spt5 is involved in many aspects of mRNA stability by virtue of its association with many components of the transcriptional machinery, many of the genes differentially expressed in fogsk8 mutants might represent mRNA processing targets of Spt5.

Typical microarray experiments measure the concentration of thousands of mRNAs simultaneously.

In 2009, Dudley et al. [ 23] evaluated 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, and found evidences of a general, pathophysiological concordance between microarray experiments measuring the same disease in different tissues.

Conventional DNA microarray experiments measure transcript levels of mRNA molecules in arbitrary units, and hence any kinetic parameters in a fitted model result in unit free as well as quantities of regulatory proteins.

Co-expression interactions are calculated from the MegaYeast expression dataset (Gasch, 2012), which contains 501 yeast microarray experiments measuring expression during stress responses, sporulation and different cell cycle phases.

We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.