Figure 7 indicates an example of predictions where ring samples at 7% and 7.4% parasitemia were used as unknown and a PLS model in the range of 5 10% parasitemia was used; the average standard error of the prediction-deviations was 0.08% at factor 1.
In a practical situation, the embedding dimension (d) and the number of nearest neighbours (k) are estimated by minimizing the in-sample prediction errors, where (din left{ {2,3,ldots,8} right} ) and (kin left{ {1,2,ldots,20} right} ) is the percentage of neighbours with respect to the sample size.
The spatial pattern in prediction uncertainty mimics that of the predictions, where high levels of prediction uncertainty coincide with high predictions and vice versa.
The predicted utility scores with different levels of transfusion frequency were estimated using the parameter estimates from the conditional logit model and out of sample prediction of utility scores assuming scenarios where all treatment alternatives required 0, 4, and 8 transfusions per month, respectively.
DF assigned a probability value for each prediction, where samples with the probability value ≥ 0.5 were designated as cancer samples, whereas others were designated as normal samples.
ANFIS is a type of adaptive multi-layered feedforward networks [23], applied to nonlinear prediction where past data samples are utilized to predict the data samples ahead.
A supervised Artificial Neural Network (ANN) has been trained and validated, based on input-layer of geophysical well-logs and an output-layer of core-lithofacies, for lithofacies prediction where core samples were not available.
Fig. 15 Sample prediction results (unpredictable pattern).
Fig. 13 Sample prediction results (periodic pattern) Fig. 14 Sample prediction results (growing pattern).
This is well in line with the multimodal aspect of protein structure prediction, where failure to sample suboptimal, but populated conformers may prevent understanding of biological mechanisms involving protein flexibility.
