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.
Highlights
In recent years, the long hours and high costs of developing new drugs have been significant constraints (DiMasi et al, 2003; Adams and Brantner, 2006)
We propose a novel computational model based on Multi-graph representation learning (MGRL) to predict drug-disease associations, which is mainly divided into three parts
The case study shows that the model MGRL could better help medical researchers discover new drug-disease associations
Summary
The long hours and high costs of developing new drugs have been significant constraints (DiMasi et al, 2003; Adams and Brantner, 2006). Graph Representation for Predicting Drug-Disease correlations by constructing similar characteristics of recently known drug-disease associations. Luo et al (2018) proposed a drug repositioning recommendation system to predict new drug-disease associations by constructing a heterogeneous drugdisease interactions network. Wang et al (2014) designed a computing framework based on a heterogeneous network model to calculate the similarity between drug pairs of diseases through heterogeneous graphs of drug-target information. Wang et al (2019) proposed a prediction method for embedding drugdisease associations networks using graph neural networks. Based on the similarity between drugs and diseases, Yu et al (2020) introduced graph convolutional neural networks to predict potential drug-disease associations. We propose a novel computational model based on Multi-graph representation learning (MGRL) to predict drug-disease associations, which is mainly divided into three parts. The case study shows that the model MGRL could better help medical researchers discover new drug-disease associations
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