Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 20, Issue 2, Pages 1506-1517Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2022.3212051
Keywords
Drugs; Diseases; Computational modeling; Predictive models; Collaboration; Training; Shape; Drug discovery; computational drug repositioning; outer product; positive-unlabeled learning
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Computational drug repositioning technology is an effective tool for accelerating drug development. However, existing models have limitations such as a large number of unvalidated drug-disease associations and issues with the inner product. This study proposes a novel PUON framework to address these deficiencies and extensive experiments on four real-world datasets showed that the PUON model achieved the best performance based on six evaluation metrics.
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 6 popular baselines. Extensive experiments in four real-world datasets showed that PUON model achieved the best performance based on 6 evaluation metrics.
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