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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 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.
Similar(52)
We provide evidence that our methods have the potential to enhance target selection through rapid functional screening assays and the effective triaging of drug candidates prior to in vivo studies.
We present experimental evidence which suggests that our method offers high levels of performance and compares favourably with competing state-of-the-art techniques.
Thus, we conclude that our computational mappings are largely consistent with previous experimental data of Tonzetich et al. (1990), providing evidence that our computational method is accurately detecting orthologous tRNA genes.
Experimental evidence on various data collections verified that our method is faster than retraining the classifier from scratch, while the achieved classification accuracy rate is maintained at the same level.
In no case was a single mRNA identified as a target of a "fake" RBP (at an FDR <20%), further evidence that our analysis methods are sufficiently stringent.
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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