  Traditional drug discovery methods are costly and time-consuming. Drug
repositioning (DR) is a common strategy to overcome these issues. Recently,
machine learning methods have been used extensively in DR problem. The
performance of these methods depends on the features, representations and
training dataset. In this problem, feature sets include many redundant
features, which have a negative effect on the performance of methods. Moreover,
selecting an appropriate training set is influential in the rise of machine
learning method accuracy. However, in this problem, we face two obstacles to
find the proper training set. First, most methods employ known and unknown
drug-disease pairs as positive and negative sets, respectively. While the
number of known pairs is much less than unknowns, it leads to machine learning
performance error because of biasing to the majority group. Second, the absence
of a drug-disease association means this association has not been approved
experimentally and may be changed. In this paper, DRP-VEM framework is proposed
to overcome the challenges. We assess DRP-VEM based on different parameters:
disease and drug feature representations, classification methods, and voting
ensemble training approaches. DRP-VEM is evaluated using heterogenous
evaluation criteria. Moreover, we compare DRP-VEM using the best combination of
parameters with DisDrugPred.
