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We show that both models can identify a rich set of network gene interactions.
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Reaction engineering, via mathematical models, can identify key catalyst design variables and quantify their optimum values.
These models can identify spatiotemporal changes in effective connectivity during distinct cognitive states and allow for examining how effective connectivity is shaped by NTBS.
Richard Wexler, Executive Director of NCCPR, and author of the NCCPR report, casts doubt on the idea that predictive models can identify specific children at greatest risk.
On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.
If the models performed well in this first experiment, we have confidence that the models can identify structured missingness.
Conclusions Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years.
While animal models can identify potential mechanisms of particle effects, the relative importance of these pathways in humans at lower doses is not clear and may be determined by examining subjects with genetically determined differences in oxidative-stress defenses.
A new computer model can identify the best solutions to complex tradeoffs in vineyard management.
Here we show that computational modeling can identify spurious non-local components of atrial fibrillation electrograms and improve activation mapping.
This model can identify the optimal water content using only one experimental result.
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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