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P-value is a measure for evaluating the significance of the LP-score of d ij, which is computed through a randomization experiment with a set of 1000 random networks as reference.
Deducing from the above mentioned randomization experiment (Table S7 of Additional file 10), we expect the first case to be less frequent than the second.
Additionally, the randomization experiment, in general, obtained better coverage than the improved experiment with the same number of tests (best results: increase of 8%% on average branch coverage for ECS partitioning), showing that the randomization feature improves testing results.
That is, the small p‐values imply that in the randomization experiment, there is not any strong signal.
In order to assess whether the cyclic orderings obtained using QNet and NeighborNet reduce the fraction of uninterpretable variation, we performed the following randomization experiment.
We then performed a randomization experiment (10,000 times) to compare the density of lineage-specific enhancers with that anywhere else in the genome.
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We also performed randomization experiments by randomly shuffling promoters among the genes to create 1000 randomized data sets, and then performing module discovery and annotation analysis of each of these.
The models presented are thoroughly validated by crossvalidation, randomization experiments, bootstrapping, and training set/test set partitioning.
We would also acknowledge the suggestion of one reviewer to include randomization experiments.
As estimated by additional randomization experiments in Table S7 of Additional file 10, false discovery rates amounted to much smaller mean fractions of 0.02 ± 0.01 and below for the positive and negative ionization modes, respectively.
This issue has been fixed in BETA 2.0, which generated test cases for all 13 modules using all input space partitioning and logical coverage strategies (improved and randomization experiments).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com