Different properties of sequences like amino acid composition and dipeptide composition, and PSI-BLAST results were used for training and optimization of the classifiers.
This has profound implications for the design and optimization of training populations.
The training set was used for the development and optimization of the algorithm, and the test set was used to validate the algorithm.
The use of in-shoe plantar pressure analysis for evaluation and optimization of diabetic footwear requires (extra) investments in machinery, measurement equipment, personnel, and training.
Recommendations for improving PS include development of general protocols for PS, training of doctors at all levels and optimization of ED facilities and staffing.
Once development was used for optimization of classifiers or feature selection, the training and development sets are united for training.
It provides a number of parameters and kernels for the proper optimization of model training.
However, training such an ideal model is not practical due to the need for large amount of AV training data and also due to its train and optimization complexity.
We randomly divided the sequence-unique data set into six equally sized groups, using proteins from four groups for training (optimization of junctions in the neural networks), one for cross-training (optimization of general network parameters, including "stop-training"), and one for testing (estimate performance).
The Se_verification, Sp_verification, and the value of the optimization criterion achieved for the training database with the selected thresholds for the rI, rII, rPCA, and their combinations are presented in Table 1.
