Your English writing platform
Discover LudwigExact(7)
We proceeded to individually add each experiment's expression data into the original training compendium to generate five new training compendiums.
RCs were reported for SPT3 across 5 FL treatment experiments, under 3 different training compendiums: + Δspt3 and + Δerg11/ERG11.
In this study, modified training compendiums, the training phase "variables", were created by adding of new gene deletion data to the training compendium.
Changes in gene rank (RC) between the original and modified training compendiums for FL targets, ERG11, ERG6, ERG5, and non-target SPT3, were subsequently determined.
RCs were reported for ERG11 across 5 FL treatment experiments, under 3 different training compendiums: + Δerg11/ERG11, + Δerg6, and + FL treatment.
Specifically, we generated modified training compendiums, or unique training phase variables, and examined how they altered the network's gene-gene interaction "patterns" to improve final gene ranks.
Similar(53)
We used this data as the training compendium for EPSA.
Modified gene interaction networks were then inferred for each modified training compendium.
Expression data were input into the training compendium as a single experiment file.
Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications.
First, the original training compendium of RMA-normalized Affymetrix data was used to infer the gene-gene interaction network B. This training compendium consisted of 1039 Affymetrix YG S98 GeneChips, representing 465 experimental conditions [ 12].
Write better and faster with AI suggestions while staying true to your unique style.
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