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Every circuit whose real-world behavior varies dramatically from our best predictions represents an opportunity to better understand the components, interactions, and control mechanisms of both the system and the host.
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The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations.
Each of these predictions represents a testable hypothesis.
Each of these predictions represents a potentially testable hypothesis.
The simulated and experimental values are very close, leading to the conclusion that such DEM predictions represent an accurate description of the process in a tumbling mill.
The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods.
However, it is important to point out that the accuracy of any predicted proximon is contingent on the corresponding accuracy of the coordinates of its member genes and metagenomic gene prediction represents an inherently challenging task.
The 3-nearest neighbor algorithm represents a simple local learning method whose predictions represent a baseline predictive power of asymptotic local learning approaches.
These five predictions represent a power shift in the world of healthcare, where new players emerge as forces to be reckoned with, and consumers gain greater control over their care.
In combination with the membrane constraint, proteomic predictions represent a milestone for constraint-based modeling.
We acknowledge that our predictions represent a first approximation of protein characteristics, and could be subject to over-interpretation for at least two reasons.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com