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The optimal score from the model gave a sensitivity of 81% and a specificity of 74%.
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The optimal scores from H are normalized into Z-scores as follows.
Having chosen the optimal score for each method by these means, we ranked alignments for each method into 15 bins from 99.9% down to 98.5% of alignments.
Hence, additionally to its benchmarking interests, PC provides a guidance to choose an optimal score threshold from VS results, allowing one to assess decision criteria from multiple points of view.
PC also ease the process of extracting optimal score selection thresholds from virtual screening results, which is a valuable step to proceed to prospective virtual screening.
We present an efficient method for representing all alignments whose score is within any given delta from the optimal score.
We also show that by counting the number of near optimal alignments as a function of the distance from the optimal score, we can select a good set of parameters that best constraints the biologically relevant alignments.
As in the case of the optimal score (k = 1), deviations from the theoretical form are significant only in the regime of small probabilities, which is not accessible with naive sampling methods.
In order to quantitatively compare the distribution with theoretical predictions from Karlin-Altschul statistics [ 28], we used the estimated Gumbel parameters λ and s0 from the optimal score distributions.
The choice of K=10 is computationally beneficial given that even for such low K, the score for the next optimal solution drops off fast at lower γ, but for γ=0.5 the optimal score only changes by 0.02% (from 16774.7 to 16771.2) after 50000 solutions are considered.
From now on, I will also call these bifurcating trees of optimal score supertrees, since they contain all taxa from the input trees.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com