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The majority methods for drug/compound target prediction are based on chemical structure, including the chemogenomic approach.
Note that the Average and Majority methods produce the value for metrics based on classification error (such as Accuracy and TPR), but differ in metrics producing real values (such as RMSE).
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Based on this result, it would seem natural to search for another voting procedure that differs from the majority method in not giving rise to this form of cycles.
However, for purposes of this preliminary study, using the majority method seemed sufficient.
This is because the Average method uses real values output from the individual models, while the Majority method uses voting.
In contrast, the majority method considers all classifiers equally relevant and takes a vote, deciding for class that agrees with the majority.
Another approach would be to apply a "majority method" that selects only variables that were retained in all imputed data sets.
Alternative analysis methodologies that do not utilize the mismatch controls may alter the discordant set, but as described earlier, there is a large potential variation in the different methodologies and a lack of a single majority method.
In general, for samples with high tumor cell contents, the majority genotyping methods are suitable; however, for samples without sufficient tumor cell contents, more sensitive methods are preferable.
In addition, we showed that, by employing a DC method, we are able to lower this effect to some extent for the majority of methods used.
This class can be labelled as main, as it contains majority of methods providing program functionality.
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