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Recently, the iterative MSFV (i-MSFV) method was introduced to iteratively improve the localization assumption, and it was shown that the method converges to the fine-scale reference solution.
We carried out three field experiments to iteratively improve the concept.
Here we use PrimerROC analyses to evaluate and iteratively improve the performance of our ΔG-based dimer-prediction algorithm, PrimerDimer.
ROC analysis was used to assess and iteratively improve the dimer prediction performance of the PrimerDimer algorithm.
A random optimization algorithm is used to iteratively improve the solution.
A strategy of evolutionary optimization was implemented to iteratively improve allosteric coupling.
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They show that minimizing the disagreement in predictions between these models leads to improved accuracy, and introduced a co-updating technique for iteratively improving prediction concordance.
The algorithm uses high-scoring sets of similarly expressed nodes as seed data and iteratively improves sets of seed genes by considering the addition of connected nodes and improvement in module score.
These computational coordinates can be iteratively improved and incorporated in MS assay libraries and re-used in future experiments.
Our work has been field tested and iteratively improved over the past six years at our Stanford programs and convenings worldwide.
These problems can be corrected by iteratively improving the zeroth order solutions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com