Sentence examples for predictions with accuracy from inspiring English sources

Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction.

However, investigation of the Bayesian optimization approach to parameter selection revealed that the choice of parameters tested was justified, and hence, the results obtained with their use were significantly better than those obtained with the other approaches 75% predictions with accuracy over 0.7.

FEATURE correctly identified only two site-bound Mg2+ ion positions within its seven top-scored predictions with accuracy of 1.5 and 3.6Å, respectively.

This demonstrates that prediction accuracy is greatly dependent on the scale of prediction, with accuracy of an individual plot being far poorer than accuracy at a regional scale.

The CSIDOP method is capable of making protein function prediction with accuracy of 95.42% using 2,972 gene ontology (GO) functional categories.

With these data Fagotti et al. developed a prediction model with accuracy for prediction of unsuccessful debulking between 69% and 75% depending on the cutoff level of the Predictive Index Value used [ 17].

A good agreement was obtained between the measured values and the correlation predictions with ±5% accuracy.

Compared to PCM values, the NCM formalism provides λ(1 max predictions with equivalent accuracy and reliability.

Simulations of fluctuations in the inflation field can explain the formation of galaxies; several experiments, set up to either verify inflation or test alternative theories, have generated data that match Linde's predictions with great accuracy.

Simulations of fluctuations in the inflaton field can explain the formation of galaxies, and several experiments, set up to verify various versions of inflation, have generated data that match Linde's predictions with great accuracy.

The linear and polynomial kernel-based SVMs provided predictions with least accuracy and therefore were not recommended for genome wide prediction of discrete traits.