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Link Prediction based on Tensor Decomposition for the Knowledge Graph of COVID-19 Antiviral Drug
Data Intelligence ; 4(1):134-148, 2022.
Article in English | Web of Science | ID: covidwho-1677465
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.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: Data Intelligence Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: Data Intelligence Year: 2022 Document Type: Article