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In summary, for the calculations regarding the wind power generation, the values we generated were in good agreement with the literature.
In order to assess the statistical significance of this value, we generated 1000 random matrices and found their entropies to be distributed normally, with mean entropy of 2.7×10−4 and a SD of 3.8×10−5.
To compute the p value, we generated a random distance distribution for each of the 4613 targets in each of the ten fingerprint spaces by computing distances between the ChEMBL compounds associated with the target and randomly selected molecules from the ZINC database [52], taken as representative molecules of screening compounds for which target predictions might be carried out (Fig. 1h).
For each SNR value, we generated 50 synthetic signals.
Using the estimated parameter values, we generate a simulated data set that mimics the Native American data, and we employ this simulated data set to evaluate the performance of our model.
Based on the E values, we generated similarity clusters representing gene families with the Markov clustering program (van Dongen 2000).
To address the nondeterminism of the algorithms/tools, related to the the ordering input of parameters and values, we generated test cases with 5 variations in the order of parameters and values, and took into account the average of these 5 assessments for the statistical tests.
In addition to the F-value and p-value, we generated eta-squared as a measure of the effect-size.
To check the models sensitivity to initial parameter values, we generated 100 sets of random parameters and started training from these parameters.
To obtain the target values AHD, we generate a wild-type network and a mutated network with a single mutated node.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com