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For each ID we also collected the entire editing history in all linked language editions.
To find out which genes were most consistently expressed across these samples, we used the coefficient of variation – defined as the standard deviation divided by the mean – of the expression values for each ID, calculated across all stem cell samples.
For each ID there was a closed envelope with the group assignment.
This repeated sampling was represented as a "Pre-Post" factor effect with two types "Before" or "After" representing the two scores for each ID.
We utilized Rank Product analysis, a non-parametric method based on geometric mean of fold changes, to produce a fold-change, statistics and a ranking for each ID.
You should have two files for a paired-end experiment for each ID, fastq/SRR1039520_1.fastq1 and fastq/SRR1039520_2.fastq, which give the first and second read for the paired-end fragments.
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When the compilation of the folders was completed, there were three folders for each subject: ID labelled (8), ID labelled (6) and ID labelled (4) containing 8, 6 and 4 intra-oral photographs respectively.
For each protein ID, via the mRNA ID, we were able to identify the appropriate HGNC HUGO symbol and compare it to the Babelomics- and NCBI-derived symbols.
For each Protein ID the corresponding Nucleotide ID was retrieved from UCSC genome database (genome releases hg19 and mm9), along with its exon annotation.
The training samples were generated as follows: for each gene ID candidate, if the ID appears in the manual annotation list, the candidate is taken as positive, otherwise negative.
When the congestion has occurred, a congestion notification message, which contains the node id and path id, is sent back to the sources for each path id known by the node.
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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