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This is done to assess the model performance in a situation similar to a real microarray experiment where multiple samples are used (i.e. samples from different tissues or a time series experiment).
This approach corresponds to a microarray experiment where the promotor or protein states are not observed.
The PCR quantitation was carried out for individual animals, unlike the microarray experiment, where the same samples were pooled.
To elucidate this property further, we carried out another set of microarray experiment where we blocked nuclear translocation of HIPPI by knocking down its transporter HIP1 [ 9].
The choice of probability distribution was based on a histogram of gene expression values from a real microarray experiment, where it could be observed that the values approximately followed this distribution (Fig. 4).
This dataset is a microarray experiment where global gene expression of three tissues (GFP, whole brain, and liver) from 4 cast/cast and 4 b6/b6 F2 mice that were non-recombinant for the entire HG2D congenic donor region was compared.
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The statistical issues become even more pronounced when transitioning from static microarray data to temporal microarray experiments where the gene expression levels are traced over a period of time.
This is different for microarray experiments where cDNA synthesis is initiated at the 3' end of the mRNA.
This differs from microarray experiments, where probe intensities for measuring transcript expression are independent of each other [ 3].
In the context of microarray experiments where thousands of genes are commonly evaluated simultaneously, the probability of detecting false positives rises sharply.
Thus, we used the Gene Chaser tool[ 54] to search for microarray experiments where all three human P-DUDES genes would show similar expression changes.
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