Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

Zhang, Yuchen; Lei, Xiujuan; Pan, Yi; Wu, Fang-Xiang

doi:10.3389/fphar.2022.872785

Cited by 12 publications

(10 citation statements)

References 61 publications

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“…We predicted the top 10 possible anti-covid-19 drugs, as shown in Table 5 , and 6 of them can be found in the relevant literature. Since the three datasets we used do not contain data on COVID-19 and antiviral drugs, we used the HDVD database mentioned in Zhang et al (2022) . According to Li et al (2021) , glycyrrhizic acid (GA) is clinically an anti-inflammatory drug against inflammatory stress caused by pneumonia, and the combination of glycyrrhizic acid and vitamin C can serve as a potential treatment for COVID-19 Treatment options.…”

Section: Methodsmentioning

confidence: 99%

“…We predicted the top 10 possible anti-covid-19 drugs, as shown in Table 5, and 6 of them can be found in the relevant literature. Since the three datasets we used do not contain data on COVID-19 and antiviral drugs, we used the HDVD database mentioned in Zhang et al (2022). According to Li et al (2021), glycyrrhizic acid (GA) is that favipiravir has a high potential to treat COVID-19, especially in patients with mild to moderate disease.…”

Section: Case Studiesmentioning

confidence: 99%

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Drug repositioning based on heterogeneous networks and variational graph autoencoders

Song

Lei

2022

Front. Pharmacol.

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Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly. The emergence of more and more artificial intelligence-based drug repositioning methods in the past 2 years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder. First, a drug-disease heterogeneous network is established based on three drug attributes, disease semantic information, and known drug-disease associations. Second, low-dimensional feature representations for heterogeneous networks are learned through a variational graph autoencoder module and a multi-layer convolutional module. Finally, the feature representation is fed to a fully connected layer and a Softmax layer to predict new drug-disease associations. Comparative experiments with other baseline methods on three datasets demonstrate the excellent performance of VGAEDR. In the case study, we predicted the top 10 possible anti-COVID-19 drugs on the existing drug and disease data, and six of them were verified by other literatures.

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Section: Methodsmentioning

confidence: 99%

“…We predicted the top 10 possible anti-covid-19 drugs, as shown in Table 5, and 6 of them can be found in the relevant literature. Since the three datasets we used do not contain data on COVID-19 and antiviral drugs, we used the HDVD database mentioned in Zhang et al (2022). According to Li et al (2021), glycyrrhizic acid (GA) is that favipiravir has a high potential to treat COVID-19, especially in patients with mild to moderate disease.…”

Section: Case Studiesmentioning

confidence: 99%

Drug repositioning based on heterogeneous networks and variational graph autoencoders

Song

Lei

2022

Front. Pharmacol.

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“…GNN is also being widely used for drug repositioning. Lei's team (Zhang et al, 2022b) proposed a new method based on Graph SAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. The team (Lei et al, 2022) also proposed a drug repositioning method for predicting drug-disease associations using a graph auto coder.…”

Section: Research Hot Spots and Trendsmentioning

confidence: 99%

Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis

Yao,

Shen,

et al. 2024

Front. Pharmacol.

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IntroductionIn recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research.MethodsPublications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies.Results and DiscussionIn this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.

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“…In addition, as the number of neighbors of a vertex can vary from one to a thousand or even larger, it is inefficient to take the full size of a vertex's neighborhood. GraphSage [68] adopts sampling to obtain a fixed number of neighbors for each node and replaces Equation ( 19) by a general aggregate operation, and the graph convolution is performed by…”

Section: Spatial-based Gcn U Pmentioning

confidence: 99%

Network Learning for Biomarker Discovery

Ding¹,

Fu²,

Luo³

et al. 2023

IJNDI

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Survey/review study Network Learning for Biomarker Discovery Yulian Ding 1, Minghan Fu 1, Ping Luo 2, and Fang-Xiang Wu 1,3,4,* 1 Division of Biomedical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada 2 Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada 3 Department of Computer Sciences, University of Saskatchewan, S7N 5A9, Saskatoon, Canada 4 Department of Mechanical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada * Correspondence: faw341@mail.usask.ca Received: 14 October 2022 Accepted: 5 December 2022 Published: Abstract: Everything is connected and thus networks are instrumental in not only modeling complex systems with many components, but also accommodating knowledge about their components. Broadly speaking, network learning is an emerging area of machine learning to discover knowledge within networks. Although networks have permeated all subjects of sciences, in this study we mainly focus on network learning for biomarker discovery. We first overview methods for traditional network learning which learn knowledge from networks with centrality analysis. Then, we summarize the network deep learning, which are powerful machine learning models that integrate networks (graphs) with deep neural networks. Biomarkers can be placed in proper biological networks as vertices or edges and network learning applications for biomarker discovery are discussed. We finally point out some promising directions for future work about network learning.

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Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

Cited by 12 publications

References 61 publications

Drug repositioning based on heterogeneous networks and variational graph autoencoders

Drug repositioning based on heterogeneous networks and variational graph autoencoders

Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis

Network Learning for Biomarker Discovery

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