Sentence examples for interaction predictions from inspiring English sources

These methods use an assembly of features from protein pairs to learn the difference between interacting and non-interacting pairs, and then score the new interaction predictions.

The second strategy for RNA-RNA interaction predictions is usually handled with a class of approaches that simultaneously predict a common structure for both RNAs including their interaction.

In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions.

To the best of our knowledge, this is the first study where the KIBA dataset was used for the evaluation of drug target interaction predictions, and we believe that it should be used in future studies as a benchmark set, because of its heterogeneous and well-balanced nature.

Hence, the assumption of independence may strongly bias the interaction predictions in our case.

Such data integration improves the coverage and the specificity of interaction predictions [2], [9].

This is further supported by the observation that only a subset of the AAC features important for interaction prediction were enriched in known interacting domains.

In protein interaction prediction, it mainly focuses on identifying interacting domains.

We now define the problem of predicting RNA-RNA interaction as follows: (RNA RNA interaction prediction).

STRING (17), Human Protein Protein Interaction Prediction (PIPS) (18) and Interologous Interaction Database (I2D) (19) collect and predict interactions and include BIND as an interaction source.

The value of protein interaction prediction and gene association mining tools lies in their ability to predict reliable novel interaction and association candidates.