Sentence examples for the prediction of missing from inspiring English sources

In other words, if the missing signs have specific pattern, the prediction of missing values would performed.

Some efforts have also been dedicated to improve the prediction of missing data, based on correctly received ones (see, e.g., [14, 22] for details).

The imputation literature recommends including not only pre-exposure patient characteristics and treatment information in the prediction of missing values but also information on the outcome status [ 12].

This corresponds to rows 7 9 and 12 in Tables 1 4 and simulates the prediction of missing drug target interactions in the known bipartite network.

By incorporating similarity at both the sequence and the network level, we compute alignments that allow for the transfer of functional annotation as well as for the prediction of missing interactions.

In our specific example the combination of primary and secondary prevention with lipid-lowering medications seems to complicate the prediction of missing values, but in the end is likely a reason why we could differentiate so well between patients who have an outpatient LDL test performed versus not.

To evaluate the performance of prediction of missing links, the known data are randomly divided into two parts, the training set and the probe set.

As an example, the prediction of a missing myo-inositol:oxygen oxidoreductase reaction led to the design of a simple experiment in which the previously uncharacterized gene yhiJ was found to be essential for growth on myo-inositol.

For each team the overall score Score1 combining both parameters and protein values is defined as Scor e 1 = − log p 1 prot ⋅ p 1 param For model 2 we requested the prediction of 3 missing links of the network as shown in Additional file 2: Figure S2A.

We focus here on the third experimental condition: prediction of missing drug target interactions in the known bipartite graph, as these results are those which gave a significant improvement with respect to Yamanishi et al. (2008).

The assumption is not testable, but it can be made more plausible by using a more inclusive strategy in which more variables are included in the imputer model for prediction of missing values.