Drug Repurposing for the Treatment of COVID‐19: A Knowledge Graph Approach

Yan, Vincent Ka Chun; Li, Xiaodong; Ye, Xuxiao; Ou, Min; Luo, Ruibang; Zhang, Qingpeng; Tang, Bo; Cowling, Benjamin J.; Hung, Ivan; Siu, Chung Wah; Wong, Ian C K; Cheng, Reynold; Chan, Esther W.

doi:10.1002/adtp.202100055

Cited by 15 publications

(7 citation statements)

References 37 publications

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“…Fang et al [71] suggested a head-and-tail entity fusion model, which obtained 97% accuracy while fusing data from diverse sources. Yan et al [104] created a COVID-19 KG by integrating 14 publicly available bioinformatic databases comprising information on medications, genes, proteins, viruses, illnesses, and symptoms and their associations. They utilized the DrugBank ID for each drug, the National Center for Biotechnology Information gene ID for each gene, and the MeSH ID for each disease because they are all standardized.…”

Section: Knowledge Fusionmentioning

confidence: 99%

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Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Duan

Pan

et al. 2023

Big Data Min. Anal.

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Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

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Section: Knowledge Fusionmentioning

confidence: 99%

“…However, combining various reasoning models is also a successful strategy; Yan et al [104] employed motifbased graph analysis (GM-based) and KG embedding (KRL-based) to compute the scores for candidate drugs independently and then combine them using a linear function.…”

Section: Reasoning Based On Krlmentioning

confidence: 99%

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Duan

Pan

et al. 2023

Big Data Min. Anal.

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“…In their conclusion, they recommended using a classifier developed on PubMed-BERT (a variant of BERT which is a transformer-based machine learning technique) to construct a COVID-19 knowledge graph and then applying TransE (a neural knowledge graph completion algorithm) to predict drug repurposing candidates. Yan et al [13] constructed a knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, and symptoms and developing their linkages; and then generated and ranked drug candidates for repurposing as treatments for COVID-19 by integrating motif scores, PageRank scores, and embedding scores for each drug. Al-Saleem et al [14] constructed the "CAS Biomedical Knowledge Graph" using data from the "CAS Content Collection" and other public repositories; and then used their own result ranking method to predict potential drug repurposing candidates for COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case

Hamed¹,

Jończyk²,

Alam³

et al. 2022

Preprint

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This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature; (2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction; (3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects.

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“…A similar study was carried out by Zhou et al [ 61 ], where high-value proteins and drug combinations were derived by a network-based algorithm. Yan et al proposed a knowledge graph approach to prioritise drug candidates against SARS-Cov-2 [ 62 ]. This study integrated 14 biological databases of drugs, genes, proteins, viruses, diseases, symptoms and their linkages, and developed a network-based algorithm to extract hidden linkages connecting drugs and COVID-19 from the constructed knowledge graph.…”

Section: Mining Patient Data and Drug Repurposingmentioning

confidence: 99%

“…Motif-clique discovery algorithms are used to extract these defined motifs-of-interest. Credit: Yan et al /Wiley [ 62 ]. (Online version in colour.)…”

Section: Mining Patient Data and Drug Repurposingmentioning

confidence: 99%

Data science approaches to confronting the COVID-19 pandemic: a narrative review

Zhang

Gao

et al. 2021

Phil. Trans. R. Soc. A.

Self Cite

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During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

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Drug Repurposing for the Treatment of COVID‐19: A Knowledge Graph Approach

Cited by 15 publications

References 37 publications

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case

Data science approaches to confronting the COVID-19 pandemic: a narrative review

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