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To some extent these approaches predict the obvious.
It was shown that all three approaches predict the global deformation behaviour in both tension and bending compared with experimental data.
These single-step MM-PB/SA and MM-GB/SA approaches predict the experimentally most potent ligand as first- or second-ranked according to total binding free energy.
The state-of-the-art QA resource allocation approaches predict the defect-proneness of an artifact using the historical data of different software metrics, e.g., the number of previous defects and the changes in the artifact.
From this, it follows that both approaches predict the same BCR-ABL1 fusion.
Most of the existing computational approaches predict the pathogenic effect using statistical methods, machine learning techniques, or protein evolution models, based on features such as sequence homology, biochemical properties, and structural information (hydrogen-bond network, solvent accessibility, and so on).
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The three DSM approaches predicted the SD trend mainly from the covariates derived from the digital elevation model (DEM).
In any of these approaches, predicting the vector dose that optimizes safety, particularly in vector-naïve individuals, yet provides optimal immunogenicity will facilitate the design of clinical trials.
However, neither of these approaches predicts the direction of change in the concentration of a metabolite, one of the main benefits of E-Flux-MFC E-Flux-MFC E-Flux-MFC
The recycled predictions approach predicts the outcome (testing) assuming that everyone in the dataset was treated as if they first did and then did not have RA.
This approach predicted the uracil salvage pathway should be under circadian control, and this prediction was experimentally demonstrated.
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CEO of Professional Science Editing for Scientists @ prosciediting.com