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In order to interpret the value of the predictions, we calculated the positive predictive value (PPV) and negative predictive value (NPV).
To test these predictions, we calculated wA wB at the time of every choice and arranged choices in order of increasing weight difference for the sigmoid action selection method.
For both predictions, we calculated the ROC curves, using gene disease associations contained in MGD as benchmark dataset.
To evaluate the accuracy of co-expression predictions, we calculated a function similarity measure that described how well the two genes in a gene pair were associated according to biological expert knowledge.
> From all resulting predictions, we calculated different performance parameters, such as recall and precision by adapting an evaluation protocol used for the comparison of a large number of TSSs predicting methods (Abeel et al., 2009).
To further check the specificity of our predictions, we calculated precision and recall curves for identifying the SAGA interactions from the total of the possible interactions of SAGA units with other proteins.
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To evaluate our predictions, we calculate the ratio of experimentally validated PPIs from FANTOM and BioGRID databases [ 9, 23] in the set of our candidates.
To assess the quality of domain boundary predictions, we calculate precision and recall rates, the normalized domain overlap (NDO -score (Tai et al., 2005) aNDO -scoreain boundary disTaicet(DBD) score (Tress et al., 2005).
To cluster the dataset molecules and for the nearest-neighbour prediction, we calculated all pair-wise molecular similarities using our in-house similarity tool GMA [31].
These 27 common features are provided in Table 2. To identify what kinds of features are important for translation rate prediction, we calculated the numbers of each kind of features in the optimal feature set. Figure 2 shows the numbers of each kind of features in (A) the optimal 37-feature set of rich condition, (B) the optimal 86-feature set of starvation condition.
As a measure of the accuracy of GEBV prediction we calculated the correlation between realized EBVs and GEBV predictions (rEBV,GEBV).
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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