Exact(24)
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.
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].
ERG11 RCs for all FL treatment experiments dropped substantially when ERG11 deletion expression data was added to the training compendium.
Inclusion of ERG6 deletion expression data into the training compendium also improved prediction of ERG6 perturbations across all FL experiments.
Similar(36)
RCs were reported for ERG11 across 5 FL treatment experiments, under 3 different training compendiums: + Δerg11/ERG11, + Δerg6, and + FL treatment.
RCs were reported for SPT3 across 5 FL treatment experiments, under 3 different training compendiums: + Δspt3 and + Δerg11/ERG11.
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.
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.
When a neural network is trained on a compendium of data, it builds a predictive model based on those data, by reflecting a minimization in error when the network's prediction (its output) is compared with a known or expected outcome.
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