Sentence examples for predictions and parameters from inspiring English sources

The PI model, its predictions, and parameters have been used in a number of ways to derive biological insight.

Having determined the posterior distribution of model predictions, there is now a direct link between different predictions and parameters, which can be exploited by determining how predictions relate to each other and to the model parameters.

Therefore it is important to have both point predictions and parameter estimations.

While this issue invalidates many of the reported evaluation results, it also impedes pre-deployment performance prediction and parameter adjustment.

We have used cross-validation and an additional base pair shifting simulations to show that binding preference prediction and parameter optimization do not result in any (optimistic) bias, or overfitting, in binding prediction accuracy.

The filter coefficients are updated when prediction and parameter errors are larger than user-specified bounds.

Therefore, we first evaluated all LUR models using a bootstrap approach to determine the sensitivity of model prediction and parameter estimates to monitor sampling.

Our proposed approach, GM-SMCC, utilizes the generative model with both network and label regularization for protein function prediction, and parameter estimation is different from original PLSA [ 31] or previous work utilizing PLSA with manifold learning for unsupervised data clustering [ 32].

After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved.

Note that this numerical analysis is partially based on the bias-variance decomposition of the estimated model predictions and estimated parameters, thus it requires the knowledge of the nominal (true) parameter vector.

We explicitly discuss the trade-off between improved predictions and additional parameters.