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Based on this statistical modeling, a robust hierarchical clustering was performed using a divergence score to measure the similarity between plots.
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Beam aperture was controlled using a divergence slit of 0.6 mm and a primary 4° Soller.
This corresponds to divergence 0.8 to 1.3 Ma (range 0.5 – 1.8 Ma) using a divergence rate of 2.3% or 3.5% Myr−1 respectively.
The position frequency matrix of consolidated motifs were compared to the matrixes within the TRANSFAC® Release 8.3 database using the T-Reg Comparator[50] analysis tool with a divergence score cutoff of 0.5.
Given a discriminator trained over the entire space of functions, this game reduces to finding the generative model minimizing a divergence score, e.g. the Jensen-Shannon (JS) divergence, to the data distribution.
For each previous study, we then noted where the reported differentially expressed genes were located in our sorted list, and assessed the resulting significance using a Z score to measure divergence from a random distribution.
Using a position specific scoring matrix as motif representation allows the definition of variable regions, which better reflects the natural divergence and thus preserves the informational content gained from the NGS sequence data.
To account for the whole spectrum of sequence divergence, we used a similarity score primarily driven by the HHsearch probability but with an additional contribution of sequence identity to properly recognize closely related phages (see Methods).
An example of such methods includes those based on moving a window along the sequence to try to identify regions with a dissimilar phylogeny using some sort of divergence score (Grassly and Holmes, 1997; McGuire et al., 1997; McGuire and Wright, 2000).
Bacterial community divergence in the mesocosms was quantified using a Euclidean-based divergence metric.
This approach corresponds to using a Itakura-Saito divergence rather than the Kullback-Leibler divergence [23].
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