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We used this set of proteins and this topology as validation network for evaluating the performance of our method.
For evaluating the performance of our method for predicting polypharmacological targets, the recall and precision were calculated.
For evaluating the performance of our method on independent data at a large scale, we generated the annotation S for 36 802 images (lateral views) obtained from the FlyExpress database (Kumar et al., 2011).
The jackknife test, which is deemed to be the most objective and rigorous protocol for evaluating predictive bioinformatics methods, was applied in evaluating the performance of our method [ 39].
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Cross-validation was performed to evaluate the performance of our method and for parameter optimization.
We have evaluated the performance of our method on three gene expression datasets, described as follows.
Hence, we additionally created synthetic ground truth data to further evaluate the performance of our method.
Here, we describe the simulation used to evaluate the performance of our method.
To evaluate the performance of our method, the proposed system is applied on medical images.
In order to objectively evaluate the performance of our method and fairly compare it with other methods, the present method was further evaluated on another independent dataset.
We evaluated the performance of our methods by two sets of simulated data and one set of patient's data derived from electronic health records.
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