Your English writing platform
Discover LudwigSuggestions(1)
Exact(4)
We have applied this classifier to RNA gene prediction in E.coli and have compared our predictions to other existing programs.
We then compared our predictions to other sets of TF interactions discovered via high-throughput experiments: physical interactions, synthetic lethal interactions, and synthetic rescue interactions (Table 1).
Because critical assumptions in complex models can be hidden in the complexity, such an examination should include a thorough sensitivity analysis that evaluates the robustness of model predictions to other plausible assumptions and data interpretations.
This feature together with the full standardization of our annotations (EFICAz always reports EC numbers rather than enzyme names; although the latter are also provided) will be very useful for comparative genome analysis and automated metabolic pathway reconstruction, and will also facilitate the incorporation of EFICAz predictions to other functional databases.
Similar(56)
I usually leave the headline-grabbing predictions to others, but even I am struggling to see how they will not get an absolute caning.
However, the RBF-NN model provides better predictions compared to other models.
(2005b) Biosystems Engineering, 92 (3), p383 390 and this study provided good predictions, compared to other equations available in the literature.
AutoMap using automatically optimized cutoffs is demonstrated to provide improved predictions, compared to other methods, in a set of immunologically relevant test cases.
In our previous work [11] we found that ASNN [44] and SVM [45] methods provided significantly higher accuracy of MP predictions compared to other tested methods while the accuracy of models developed with both methods was similar.
This classifier has improved accuracy for hub prediction relative to other traditional approaches for protein interaction prediction.
ACI 440.1-15 provided a better bond strength prediction compared to other equations with ratio of predicted to experimental values ranging from 1.03 to 1.33 for NC and from 1.18 to 1.49 for ECC.
Write better and faster with AI suggestions while staying true to your unique style.
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