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Immunohistochemistry staining of p53 has been proposed as an alternative to gene analytical method.
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For a given gene, its analytical distribution of the ratio of correlations formed by all other genes pairing this gene, is treated as a background to assess the significance of the observed values of the ratio of correlations.
This study sought to identify changes in the transcript profile of C. thermocellum ATCC 27405 grown on the substrates of pretreated Populus and switchgrass and whether these profiles were maintained across the two gene expression analytical platforms.
All recent multigene and phylogenomic studies [ 5– 8] have tested the effects of gene sampling, analytical methods, and inference programs; like our results, their topologies were more or less robust, also against varying outgroup selection.
Compared to a well-established LC protocol targeting the B1 gene, the analytical sensitivity of the novel LC protocol was evaluated with 10-fold dilution series of T. gondii genomic DNA.
For each of the 12 genes the analytical performance of the RT-qPCR assays was assessed.
By using a high-level multi-gene-focused analytical approach we believe we have overcome many of these potential limitations.
In particular, in a few hard to interpret regions or genes, different analytical protocols (or variations on the same protocol) can result in markedly different lists of variants.
By combining three gene-set analytical methods in one R statistical package, C.-Y. Chien et al. presented MAVTgsa, offering a systematic pipeline for the identification of significant gene-set modules from a set of gene expression data.
A total of 454 genes overlapped both analytical approaches and only 24 genes (1 up- and 23 downregulated) from the three biological replicate-based analysis were not present in the larger dataset from six technical replicates.
With overlaps between the gene sets, the analytical basis comprised of 62% evidence-based and 28% computationally predicted miRNA regulated genes that based on the same sources of evidence or predictions interact with 344 different miRNAs.
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