Boosting drug-disease association prediction for drug repositioning via dual-feature extraction and cross-dual-domain decoding

TL;DR

This paper introduces DFDRNN, a novel neural network model that enhances drug-disease association prediction by extracting dual features within and across domains, improving accuracy for drug repositioning.

Contribution

The paper proposes a dual-feature extraction framework with cross-domain decoding, addressing limitations of previous methods that ignore inter-relationships between drug and disease features.

Findings

DFDRNN achieves an average AUROC of 0.946 on benchmark datasets.

DFDRNN outperforms six state-of-the-art methods in drug-disease prediction.

Case studies demonstrate real-world applicability of the model.

Abstract

The extraction of biomedical data has significant academic and practical value in contemporary biomedical sciences. In recent years, drug repositioning, a cost-effective strategy for drug development by discovering new indications for approved drugs, has gained increasing attention. However, many existing drug repositioning methods focus on mining information from adjacent nodes in biomedical networks without considering the potential inter-relationships between the feature spaces of drugs and diseases. This can lead to inaccurate encoding, resulting in biased mined drug-disease association information. To address this limitation, we propose a new model called Dual-Feature Drug Repurposing Neural Network (DFDRNN). DFDRNN allows the mining of two features (similarity and association) from the drug-disease biomedical networks to encode drugs and diseases. A self-attention mechanism is utilized to extract neighbor feature information. It incorporates two dual-feature extraction modules: the single-domain dual-feature extraction (SDDFE) module for extracting features within a single domain (drugs or diseases) and the cross-domain dual-feature extraction (CDDFE) module for extracting features across domains. By utilizing these modules, we ensure more appropriate encoding of drugs and diseases. A cross-dual-domain decoder is also designed to predict drug-disease associations in both domains. Our proposed DFDRNN model outperforms six state-of-the-art methods on four benchmark datasets, achieving an average AUROC of 0.946 and an average AUPR of 0.597. Case studies on two diseases show that the proposed DFDRNN model can be applied in real-world scenarios, demonstrating its significant potential in drug repositioning.