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This tissue is the main site of expression of the gene encoding AtGA3OX4 (AtGenExpress microarray data), therefore GA20 is likely to be a natural substrate of AtGA3OX4.
When attempting to interpret the ensuing microarray data, therefore, we could be confident that the cells received functional doses of both molecules.
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The cREMaG system was designed to analyze results coming from microarray data, and, therefore, annotations for the two frequently used microarray systems, Affymetrix and Illumina, were implemented.
Microarray data were therefore checked for these cellular functions and 136 genes associated with cell cycle regulation were found to be affected similarly by both trypsin and agonist peptide.
We believe one of the strengths of our analysis is that we have not subtracted out genes thought to be specific to other cell types from our microarray data, and therefore we can detect genes that may be present in multiple cell types, even if they are more abundant in other cells than in Bergmann glia.
Microarray data analysis therefore requires dedicated algorithms and tools [ 6].
The tissue base for the microarray data does, therefore, not change in the course of the experiment.
A large body of work has studied the reproducibility of microarray data and therefore the interchangeability of commercial platforms.
Although such relationships can be tested empirically, invariably a large number of functional relationships are possible within a given microarray data set; therefore, priority setting for functional validation studies is often a challenge.
The model based approach is the best for analysing genes whose function is poorly defined (such as new potential genes from microarray data), and therefore may have the potential to be co-regulated as pairwise measurements of stability will artificially tend to select co-regulated genes [ 14].
Finding ways to reliably compare different microarray data sets is therefore important to obtain biologically sound and reproducible information from different datasets.
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