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To statistically test if the evolutionary structure of BQCV, DWV and IAPV sequences is distinct among species, we computed the association index statistic (AI) [75] and parsimony score (PS) [76] statistic of clustering strength, using the BaTS (Bayesian tip-association significance testing) program developed by Parker et al. [77].
We computed the association between Dichotomized Oxygen saturation versus dichotomized ICP using the chi square test.
For example, to examine whether sulfate was a confounder, we computed the association between ozone and mortality restricted to the days where sulfate concentrations were available.
For each dataset, we computed the association between a pair of genes as the Pearson correlation coefficient (PCC) of the two feature vectors representing these genes.
We also computed the association of G4 motifs with the Pfh1, Cdc20 and γ-H2A peaks determined here by ChIP-seq.
Once the vector representations of all documents were computed, the association between two genes, k and l, was computed as follows: (2) a s s o c i a t i o n k l = ∑ i = 1 N W i k * W i l where k = 1 … m and l = 1.
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> -wrap-foot> We computed the associations of effect size and journal impact factor with the number of citations using general linear models (procedure glm in Stata, version 11.2; StataCorp LP, College Station, Texas).
In this study, we follow these criteria and propose a modified Cosine Similarity to compute the association weight between two terms (Liu et al. 2013).
Each factor computes the association likelihood of one detection as p ( z t, j | x t, a t, j ) = N ( r t, j z ; r t, i x, Σ l h ) if object association, d clu if clutter association, (18).
The BaTS program [77] was used to compute the association index statistic and parsimony score.
This was the reaction criterion (Table 1) used for computing the association rates (see details within [26].
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