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Interestingly, using information only from line B1 to predict line B2 resulted in a comparatively smaller prediction accuracy than using only information from line B2 to predict line B1.
The correlation between Pr s and S s was higher for protein phosphorylation activation (PCC = 0.6, P-value < 0.013) than for gene set activation (PCC = 0.326, P-value < 0.051), perhaps reflecting not only a higher predictability for the protein phosphorylation data but also its smaller prediction space.
This preactivation, in turn, may hamper the differentiation between the actual action effect and the preactivation of the predicted effect, resulting in a smaller brain activity (i.e. smaller prediction error).
By contrast, the other models provide smaller prediction intervals.
As expected, a smaller prediction leap time step t d gives more accurate prediction, and as t d increases, the error distribution flattens out.
It was noteworthy that for several months (i.e. growing seasons) ordinary kriging yielded smaller prediction errors than the linear regression of temperature against elevation and locational/topographic factors did.
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Predictions in small biomass categories (< 200 Mg/ha) are comparable between the three methods, but smaller predictions > 200 Mg/ha for CRM produce 20% smaller county level estimates.
Overall, we find that RAIG makes smaller predictions that are more focused on the known target genes of the aberrations compared with GISTIC2 and GAIA.
Unlike recall and precision, these measures do not consider the number of predictions, and thus a method that fragments SCNAs into many smaller predictions can perform well on sensitivity and specificity.
The inner CV was used to train the SVM (i.e. estimate the parameters), and parameters with the smallest prediction error were used to predict the test data from the outer CV.
The simple estimation method works relatively well for small prediction windows.
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