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Using predictions for large datasets (e.g. compound libraries) allows us to overcome the data limitation problem.
Considering this effect in the iterative model significantly improved the predictions for large liquid slug void fraction conditions.
The current study suggests a combination of QSAR and MMP approaches by finding MMP transformations based on QSAR predictions for large chemical datasets.
Predictions for large proteins (> 8000 atoms) turned out to be difficult as disjunct pockets were sometimes joined by narrow "tunnels" underneath the protein surface.
In order to improve the predictions for large diameter pipelines, the effect of model parameters such as soil thermal conductivity, pipe relative roughness and velocity profile correction factor has been evaluated.
Finally, using four smaller sets of molecules, we have explored the value of expert knowledge in the review process, especially in cases where the two systems disagreed on their predictions, and the need for care when evaluating the predictions for large data sets.
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It also could up-scale the predictions for large-scale watersheds to evaluate the benefits of conservation practices.
Furthermore, the change from one host for protein engineering to another host for enzyme production becomes dispensable, and this accelerates the protein breeding cycles and makes predictions for large-scale production more accurate.
The stepwise modeling approach was shown to be necessary for providing accurate mass and heat transfer predictions for large-scale DCMD modules, providing a useful tool for the design of large-scale DCMD systems.
The height-riometer area for useful predictions thus now extends all the way to 0 dB riometer absorption (Fig. 3), but predictions for larger disturbances are still problematic.
The right panel of Additonal file 1: Figure S6 shows the relationship between the predictions for larger displacements of the end data point (Pearson correlation 0.93).
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