The Computational Drug Repositioning without Negative Sampling

TL;DR

This paper introduces PUON, a novel framework for drug repositioning that avoids negative sampling and leverages feature information, outperforming existing models on real-world datasets.

Contribution

The paper presents PUON, a new drug repositioning model that eliminates negative sampling and incorporates cross-feature information for improved accuracy.

Findings

PUON outperforms 8 baseline models on 6 metrics.

Eliminating negative sampling improves model reliability.

Using feature information enhances prediction accuracy.

Abstract

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.