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This result is not necessarily surprising, as none of the RNA structure prediction algorithms (other than RNAmutants) have been optimized to predict which mutations disrupt structure.
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Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species.
Many computational tools that are optimized to predict the impact of point mutations provide more detail.
Therefore, a more meaningful way of creating the feature weights in the AWE model is to select weights that are optimized to predict the real world criterion.
However, this approach detects the viral nucleic acids of both infectious and non-infectious viruses and needs to be optimized to predict infectivity for public health risk assessment.
These experiments inform the design principles required for synthetic polymer gels that are optimized to predict the response of specific biological tissues to impact loading, providing insight for further tuning of this gel system to match the impact response of other "soft tissues".
As a result, the models are optimized to predict the actual absolute values of the subjective quality scores.
They are optimized to predict the source of the target class, usually for each time-frequency bin.
The aggregate prognostic markers were optimized to predict progression in the KL score.
Moreover, the domain-based method can be optimized to predict the functions for an uncharacterized domain from the annotated GO terms of other domains.
These candidate gene sets were created to capture specific biological signals and are not optimized to predict unmeasured gene expression (in comparison, a GGS-generated DM set of 100 genes returned by GGS predicted 1447 genes).
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