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To quantify the number of genes captured by each platform, we used the Ensembl database to identify gene regions (defined as the start of the 5' UTR to the end of the 3' UTR), and to determine whether SNPs lie within this region).
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For a given number of top splicing candidates detected by each metric from each platform, we determine how many correspond to annotated Alt Events.
By removing the platform, we'll blunt the appeal.
To identify the systematic biases in the uncovered bases, we removed the common 71334 uncovered bases from the uncovered bases by each platform.
To do this, we examined the set difference and intersection between the exons called differentially expressed by each platform.
Table 2 summarises how the core values are addressed by each platform.
Each of these approaches requires bioinformatics solutions tailored to specific errors generated by each platform.
The annotation information was supplied by each platform at the time the experiment was conducted.
Third, the CNVs discovered by each platform are relative to the animals that are probed against.
We formatted raw genomic/proteomic TCGA data to the updated, filtered, normalized, and structured meta-data by each platform (Fig. 1).
These data sets were used for comparing regression coefficients based on the calibration curve produced by each platform.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com