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

2024-07-16

AI Summary

Existing drug repositioning methods suffer from encoding bias and inaccurate predictions due to their exclusive reliance on local neighborhood relationships, neglecting deep latent associations between drug and disease feature spaces. To address this, we propose DFDRNN, a novel deep learning framework featuring dual-domain feature extraction modules—Single-Domain Dual-Feature Extraction (SDDFE) and Cross-Domain Dual-Feature Extraction (CDDFE)—alongside a cross-bi-domain collaborative decoder, enabling joint modeling and end-to-end optimization of drug–disease similarity and association. By integrating self-attention mechanisms with graph neural networks, DFDRNN uniformly processes heterogeneous biological data. Evaluated on four benchmark datasets, DFDRNN achieves an average AUROC of 0.946 and AUPR of 0.597, significantly outperforming six state-of-the-art methods. Case studies further validate its robust repositioning capability in real-world disease contexts.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• drug repositioning
• information bias
• association accuracy

Innovation

• DFDRNN
• Dual-feature extraction
• Drug repositioning