  Computational drug repositioning technology is an effective tool to
accelerate drug development. Although this technique has been widely used and
successful in recent decades, many existing models still suffer from multiple
drawbacks such as the massive number of unvalidated drug-disease associations
and the inner product. The limitations of these works are mainly due to the
following two reasons: firstly, previous works used negative sampling
techniques to treat unvalidated drug-disease associations as negative samples,
which is invalid in real-world settings; secondly, the inner product cannot
fully take into account the feature information contained in the latent factor
of drug and disease. In this paper, we propose a novel PUON framework for
addressing the above deficiencies, which models the risk estimator of
computational drug repositioning only using validated (Positive) and
unvalidated (Unlabelled) drug-disease associations without employing negative
sampling techniques. The PUON also proposed an Outer Neighborhood-based
classifier for modeling the cross-feature information of the latent facotor.
For a comprehensive comparison, we considered 8 popular baselines. Extensive
experiments in four real-world datasets showed that PUON model achieved the
best performance based on 6 evaluation metrics.
