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• W4206586426 abstract "Abstract Due to the large-scale spread of COVID-19, which has a significant impact on human health and social economy, developing effective antiviral drugs for COVID-19 is vital to saving human lives. Various biomedical associations, e.g., drug-virus and viral protein-host protein interactions, can be used for building biomedical knowledge graphs. Based on these sources, large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses. To utilize the various heterogeneous biomedical associations, we proposed a fusion strategy to integrate the results of two tensor decomposition-based models (i.e., CP-N3 and ComplEx-N3). Sufficient experiments indicated that our method obtained high performance (MRR=0.2328). Compared with CP-N3, the mean reciprocal rank (MRR) is increased by 3.3% and compared with ComplEx-N3, the MRR is increased by 3.5%. Meanwhile, we explored the relationship between the performance and relationship types, which indicated that there is a negative correlation (PCC=0.446, P-value=2.26e-194) between the performance of triples predicted by our method and edge betweenness." @default.
• W4206586426 created "2022-01-25" @default.
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• W4206586426 date "2022-01-01" @default.
• W4206586426 modified "2023-09-26" @default.
• W4206586426 title "Link Prediction based on Tensor Decomposition for the Knowledge Graph of COVID-19 Antiviral Drug" @default.
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• W4206586426 doi "https://doi.org/10.1162/dint_a_00117" @default.
• W4206586426 hasPublicationYear "2022" @default.
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