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By its inclusion of a diverse range of evidence, our method also resonates with the growing use of mixed-methods research which appreciates the contribution of both qualitative and quantitative evidence to answering a research question.
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We provide theoretical and experimental evidence that our method is faster and requires less memory than FPPR method when the extension degree is large enough.
We have evidence that our method for reconstructing cell lineages from somatic mutations is informative and discovers relations between cells when these exist.
Furthermore the high number of known protein-protein interactions in the midnight blue module provided strong evidence that our method produced functional gene networks.
This is encouraging evidence of our method being more generally applicable, though additional evaluation should be performed to verify the effectiveness of our method in other applications, such as full text articles.
This is supportive evidence that our method can sensitively detect functional rare mutations; in other words, measuring pathway level impact of summarized rare mutational events is useful to prioritize the functional ones.
As evidence that our method is an appropriate animal model of NASH, liver histopathology similar to NASH and increased serum ALT level were induced in rats as a result of a high-fructose/high-glucose diet.
However, we have given evidence that our method is reliable, since it had a high predictive value on the Alzheimer's disease population, and an encouraging predictive value on the amnestic MCI population.
[These and other P-values in this article are corrected by Benjamini Hochberg procedure for multiple testing (BH corrected, Benjamini and Hochberg (1995).] This provides strong evidence that our method is successful in identifying rSNPs that have a functional impact on the disease and is suitable for screening large numbers of SNPs.
All of these evidences proved our method, and the perception of selecting causal variant using GWAS information is stable and effective.
We provide numerical experiments and analytical evidence for our methods and we consider an explicit medical imaging application.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com