ArticleFrontiers in genetics2021
MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A case-based explainable graph neural network framework for mechanistic drug repositioning.Bioinformatics (Oxford, England) · 2026Article
- RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.Current drug targets · 2026Article
- Herb-disease association prediction model based on network consistency projection.Scientific reports · 2025Article
- Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.BMC medical informatics and decision making · 2024Article
- Label Transfer for Drug Disease Association in Three Meta-Paths.Evolutionary bioinformatics online · 2024Article
- RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction.BMC bioinformatics · 2022Article
- Prediction of drug-drug interaction events using graph neural networks based feature extraction.Scientific reports · 2022Article
- Network approaches for modeling the effect of drugs and diseases.Briefings in bioinformatics · 2022Review
- An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing.Frontiers in pharmacology · 2022Article
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Authors and funding
6 authors.
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Abstract
Drug repositioning is an application-based solution based on mining existing drugs to find new targets, quickly discovering new drug-disease associations, and reducing the risk of drug discovery in traditional medicine and biology. Therefore, it is of great significance to design a computational model with high efficiency and accuracy. In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution network to learn the graph representation of drugs and diseases from their self-attributes. Then, the graph embedding algorithm is used to represent the relationships between drugs and diseases. Finally, the two kinds of graph representation learning features were put into the random forest classifier for training. To the best of our knowledge, this is the first work to construct a multi-graph to extract the characteristics of drugs and diseases to predict drug-disease associations. The experiments show that the MGRL can achieve a higher AUC of 0.8506 based on five-fold cross-validation, which is significantly better than other existing methods. Case study results show the reliability of the proposed method, which is of great significance for practical applications.
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