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• W3160236543 abstract "Identifying effective drug treatments for COVID‐19 is essential to reduce morbidity and mortality. Although a number of existing drugs have been proposed as potential COVID‐19 treatments, effective data platforms and algorithms to prioritize drug candidates for evaluation and application of knowledge graph for drug repurposing have not been adequately explored. A COVID‐19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID‐19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments for COVID‐19 by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif‐discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported. The proposed drug candidates serve to generate hypotheses for future evaluation in clinical trials and observational studies." @default.
• W3160236543 created "2021-05-24" @default.
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• W3160236543 date "2021-05-20" @default.
• W3160236543 modified "2023-10-04" @default.
• W3160236543 title "Drug Repurposing for the Treatment of COVID‐19: A Knowledge Graph Approach" @default.
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• W3160236543 doi "https://doi.org/10.1002/adtp.202100055" @default.
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