Therefore, the accuracy and efficiency of predictions are key factors for the prediction software.
According to Hestenes, the games of scientific explanation and prediction are key facets of model deployment.
Because making reliable predictions is a key objective in science, prediction markets offer potential benefits to scientific research [10], [11].
This result reinforces the idea that incorporating character rate evolution for gene performance predictions is a key factor and that phylogenetic informativeness metrics can be successfully applied to systematically facilitate more cost-effective phylogenetic research.
In this sense, necessary experimentation for the purpose of prediction and testing that control is key to observation and in turn observation controls theory.
The development of reliable and inexpensive in silico models for the prediction of HLA-mediated ADRs is key for patient safety and the advancement of Precision Medicine.
An understanding of the key ADME mechanisms for a particular compound together with well-defined and measured drug-specific parameters is key to prediction success.
Clearly, the quality of the CFD mesh is key for accurate predictions and an educated guess of the flow regions where severe interactions of flow structures will occur is of importance for such complex CFD computations.
Therefore, characterizing the bipolar illness trajectory is key to risk prediction and early intervention advancement.
The prediction of the peak cladding temperature is key for the evaluation of the safety characteristics of nuclear reactor designs.
