# The Computational Drug Repositioning without Negative Sampling

arXiv: https://arxiv.org/abs/2111.14696 · HTML: https://mlsanity.com/paper/2111.14696/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning](https://mlsanity.com/paper/1603.03130/) (2016)
- [Hybrid Attentional Memory Network for Computational drug repositioning](https://mlsanity.com/paper/2006.06910/) (2020)
- [The Neural Metric Factorization for Computational Drug Repositioning](https://mlsanity.com/paper/2109.07690/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [ALNSynergy: a graph convolutional network with multi-representation alignment for drug synergy prediction](https://mlsanity.com/paper/2311.16207/) (2023)
- [Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks](https://mlsanity.com/paper/2408.07233/) (2024)

## Abstract (arXiv)

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.