Sentence examples for predictions across multiple from inspiring English sources

Furthermore, these factors differentiate key biological phenotypes in breast cancer, are able to improve clinical predictions across multiple cancer data sets, and retain their predictive ability even when applied to samples taken at vastly different times or at different study centers.

Using a P-value means that predictions across multiple SNPs can be combined into a single ranked list.

The reproducibility of our modeling framework was evaluated by systematically analyzing the variability of our predictions across multiple simulations.

Interestingly, the program with lowest accuracy, Condel, combines predictions across multiple algorithms, which some observers have suggested will increase accuracy (14).

In addition, our predictions across multiple plant genomes give an estimation of the percentage of plant Golgi proteins across different plant organisms, which is in general agreement with the previous estimations.

Again, while not novel knowledge, working at this scale we added an effort to quantify the probabilistic value of these predictions across multiple host phyla, as well as compared the performance of tetranucleotide frequency to 4 other sequence composition based metrics to help provide strong guidance to researchers on using this metric.

Prediction across multiple breeds should benefit from NGS data due to an increased persistence in accuracy over generations, compared to array-based predictions [ 3].

It is suggested that the prefrontal cortex plays a critical role in coordinating the multiple levels (from genes to cells to circuits) of prediction analysis across multiple prediction centers.

As a result of the known performance problems of solubility prediction models across multiple compound series we did not attempt to calculate this property or use it as a selection filter.

Instead, it combines the hit-clustering and tree-building strategies for orthology prediction to predict orthologs and paralogs across multiple genomes.

They proposed a multi-task representation neural model to learn a shared semantic representation across multiple prediction tasks.