Sentence examples for complex predictions from inspiring English sources

Gavin et al. (2006) distinguished between core elements and modules or attachments in their protein complex predictions, but did not predict direct interactions.

The supposed reason of these differences in optimal thresholds is that BioGRID has large number of predicted PPI, which leads to higher false positive complex predictions.

In this article, we presented an approach for post-processing protein complex predictions to allow for a more detailed analysis and comparison of complexes predicted from affinity purification results.

How can one tractably model and learn to make such complex predictions?

The simplest prediction would be that neighboring pixels all share the same value (the same gray scale value, for example), but much more complex predictions are also possible.

James Zou, PhD, assistant professor of biomedical data science: He develops novel machine-learning tools that enable researchers to make complex predictions and quantify disease mechanisms using population genomics and epigenomics data.

To evaluate the performance of various protein complex prediction methods, we compare the predicted protein complexes with real protein complex data sets, APC and MPC.

Nonetheless, the performance of small complex prediction still lags behind that of predicting large complexes, especially for human complexes.

The same applies to predicted risk estimates from more complex prediction models such as diabetes risk scores.

Here we suppose that C is a set of predicted clusters of proteins by a protein complex prediction algorithm and K is a set of known complexes.

There is no difference between the connectivity distributions of complexed and non-complexed proteins in our data to justify the use of connectivity for complex prediction.