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To see how the data mining based approach performs, when only few ligand structures are available as background knowledge, we re-ran the experiments with variant B2: using only ten per cent randomly chosen from the respective DuD ligand sets (20% due to smaller ligand set sizes in case of the HMGR, ADA and TK data sets) to extract background knowledge.
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We then used extensive simulation testing to evaluate how well our approach performed when data were generated under a variety of more complicated (and realistic) scenarios.
It would be interesting to see how related approaches perform when being tailored to address such tasks.
The results show that this approach performs well when compared with traditional models and established research.
The approach performs well when applied to the prediction of CHF readmissions and is designed to be generalizable to other problem domains.
The approach performs well when evaluated against similar tools and smaller overall module size allows for more specific functional annotation and facilitates the interpretation of these modules.
This approach performs a better search when there is a tight affinity.
We find that the clustering approach performs even worse than before when LLE is used, as the accuracy is always less than 85%.
Secondly, and supporting this theoretical reasoning, analyses of simulated data have shown that the Bayesian regression approach performs slightly better than FDIST2 when scenarios deviate from the standard Island Model [ 56, 57].
From these results, it can be noticed that patch-based approaches perform better when compared with the pixel-based computation of LBP.
Generally speaking, all approaches perform similarly when allele frequency is <0.3.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com