Sentence examples for predictions we employed from inspiring English sources

Two additional sets of predictions were created from the results of datasets A and B. In order to increase temporal consistency of predictions, we employed post-processing of predicted values by temporal median filtering with a window of 11 s.

To test our predictions, we employed siRNAs to knock down the human homologs of Bc055324 (C1ORF112) and 4930547N16Rik (C12ORF48) in the widely-used HeLa cancer cell line.

To validate the computational predictions, we employed two different experimental approaches described in the literature and developed specifically for IDP identification [ 60, 61].

To improve the accuracy of the final predictions, we employ the trust (T_{u,k}) of the n neighbours.

For the KOW prediction, we employed the model previously developed and published by our team [31].

As a supplementary approach to target prediction, we employed a single leading prediction server, TargetScan Mouse (versions 5.0 and 5.1, http://www.targetscan.org/mmu_50/) using default settings to identify targets that contain 8-mer or 7-mer seed complementarity that are conserved across most mammals, but usually not beyond placental mammals (Friedman et al., 2009).

To facilitate the prediction, we employ an auto-regressive integrated moving average (ARIMA) method to predict the active periods of the machine.

In order to facilitate such prediction, we employ an auto-regressive moving average (ARMA) model along with data-driven techniques, and compare the performance of multiple data-driven techniques.

For cleavage site prediction, we employ the position-specific scoring matrix (PSSM) ϕ C proposed by Dönnes et al., which uses four C-terminal amino acids and two N-terminal amino acids to predict a cleavage site.

To test every module of ChemSAR and to build a model with high prediction performance, we employed five methods (RF, SVM, k-NN, NB, DT) to build classification models.

To better assess the prediction accuracy we employed the Matthews Correlation Coefficient (MCC) measure.