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Prediction robustness was assessed from more than 1500 diverse complexes containing homo- and hetero-dimers.
We investigate interrelated issues dealing with IMR detection and prediction, robustness, exploitation and control of IMRs using experiments and computations carried out in two model systems.
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The results are satisfactory in terms of prediction capability, robustness, and generalization.
From a statistician's perspective this problem of model selection is one of prediction and robustness.
Thus, new methods to cope with a greater number of variables and that provide accurate prediction and robustness against missing values are required.
Issues such as prediction and robustness will be critical in sifting through this data, using modeling approaches to identify and explain potential pharmacologic targets in GBM.
The method's solutions are assessed at optimal variable settings with respect to bias, quality of predictions and robustness through optimization measures, and the usefulness of those measures to select the compromise solution is evaluated.
Adverse conditions in terms of quality of predictions and robustness are simulated to evaluate the ability of desirability-based methods for yielding compromise solutions with desired response's properties.
The model predictions of robustness analysis matched the trends in differential gene expression both in vivo and in vitro.
Simulation results together with permutation tests of input gene network structures indicate that the prediction accuracy and robustness property of the two predicted networks using the top-down approach are better than those of the corresponding fully connected networks.
Therefore, we conclude that the performance of prediction has some robustness with respect to α and β.
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