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We present several alternative approaches to identify and build the key hotspot interactions within both core secondary structural elements and variable loop regions and evaluate the method's performance in natural-interface recapitulation.
To evaluate the method's performance in automatically annotating a gene set, we used previously annotated gene sets from well-known and publicly available resources.
We then describe the development of DFGmodel, a method that constructs DFG-out kinase models based on multiple structures of related kinases, and evaluate the method's performance with a variety of measures (e.g., statistical potentials).
The receiver operating characteristic (ROC) curve, which is the plot of the true positive (TP) rate with the false positive (FP) rate, depicting the relative trade-off between both rates [ 109] was used to evaluate the method's performance.
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We evaluated the method's performance using statistical power, miss-calling error rate, and precision.
We have evaluated the method's performance in comparison with two existing gold standard-based methods using a rigorous testing environment.
An SSE analysis was applied to evaluate the implemented MI method's performance with respect to bias and precision in population parameter estimates and to evaluate the method's sensitivity to η-shrinkage.
We ran the function prediction methods for sequences in this benchmark dataset and evaluated the method's prediction performances.
We evaluate the method performance on an anthropomorphic pelvis phantom and 6 pelvis patients.
The integrated graph resulting from the integration method is then compared with the original simulated true graph to evaluate the integration method's performance.
Several measurements are used to evaluate the proposed method's performance.
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