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To better interpret the amount of overall genetic variability found for harpy eagles, it is useful to compare these results with patterns of diversity found in related species [55].
For one species (Rana sylvatica), we also examined endosulfan sensitivities among six populations to estimate within-species variation in sensitivity and help interpret the amount of variation observed within versus among species.
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We interpreted the amount of each pre-miRNA as the affinity by which it was bound to its precipitated protein.
Theoretically; atomic scale computer simulations based on density functional calculations were used to interpret the different amount of solid solution and the changes in lattice parameters.
Simulations were performed to interpret the observed amount of overlap between CNVs for the German Shepherd, Labrador Retriever, and Standard Poodle samples, which were analyzed on multiple chip platforms.
To interpret the observed amount of overlap, we performed extensive simulations that recapitulate characteristics of the three aCGH chips and distribution of log2 values (see Methods).
Unbiased genome-wide analyses were performed to visualize and interpret the large amount of data produced by the sequencing and microarray experiments.
To analyze and interpret the increasing amount of sequencing data, a number of statistical methods and bioinformatics tools have been developed.
To help interpret the large amount of data generated by a standard ToF-SIMS measurement, principal component analysis (PCA) was used.
Due to a steadily increasing number of studies focusing on sAD genetics, it has become increasingly difficult to decipher and interpret the huge amount of available results.
While omic approaches offer unprecedented insight into etiology, pathogenesis, and homeostatic responses in disease, it is challenging to interpret the large amount of data generated in this manner and to distill the biologically meaningful information, including potential therapeutic targets [ 12].
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