Sentence examples for predictions were trained from inspiring English sources

We have recently combined the phi and psi angle predictions into a profile-profile alignment algorithm [30], where the input features for the angle prediction are similar as ANGLOR but both phi and psi predictions were trained by SVM [8].

Amino acid principal components and neural network (NN) predictions were trained and cross-validated on the output of a large random peptide set submitted to BepiPred 1.0 (cbs.dtu.dk/services/BepiPred).dtu.dk/services/BepiPred

Initially tools for prediction of HLA binding (often also — slightly inaccurately — called epitope prediction) were trained on data for each HLA allele independently, but the number of new alleles renders this approach more and more impractical.

The three yarn strength prediction models were trained up to a mean squared error (mse) of 0.001.

The BP yarn strength prediction models were trained using the Levenberg Marquart Backpropagation algorithm, which is one of the faster BP training algorithms used in training of prediction models (Hagan and Menhaj 1994; Demuth et al. 2005), until the set target error of 0.001 was attained.

The prediction models were trained by the libSVM software package which was written by in Chih-Jen [ 22, 23].

This could be the result of the fact that these prediction methods were trained on sets containing mostly prokaryote sequences.

The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors.

Prediction models were trained and risk scores calculated for each fold of the cross-validation, and collected together, using the opt.nested.crossval function of the pensim R package.

The parameters in the prediction model were trained using a set of experimentally validated mature miRNAs in the miRBase and further evaluated using a dataset that does not overlap with the training dataset.

This may be because the topology prediction algorithms were trained and tested on benchmark sets containing mostly prokaryotic TMPs, whose properties (e.g. amino acid composition, local structure) differ from the properties of eukaryotic and thus human TMPs.