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- W4310996987 abstract "Knowledge graph (KG) is used to represent data in terms of entities and structural relations between the entities. This representation can be used to solve complex problems such as recommendation systems and question answering. In this study, a set of candidate drugs for COVID-19 are proposed by using Drug repurposing knowledge graph (DRKG). DRKG is a biological knowledge graph constructed using a vast amount of open source biomedical knowledge to understand the mechanism of compounds and the related biological functions. Node and relation embeddings are learned using knowledge graph embedding models and neural network and attention related models. Different models are used to get the node embedding by changing the objective of the model. These embeddings are later used to predict if a candidate drug is effective to treat a disease or how likely it is for a drug to bind to a protein associated to a disease which can be modelled as a link prediction task between two nodes. RESCAL performed the best on the test dataset in terms of MR, MRR and Hits@3." @default.
- W4310996987 created "2022-12-22" @default.
- W4310996987 creator A5055807650 @default.
- W4310996987 date "2022-12-07" @default.
- W4310996987 modified "2023-10-17" @default.
- W4310996987 title "Analysis of Drug repurposing Knowledge graphs for Covid-19" @default.
- W4310996987 doi "https://doi.org/10.48550/arxiv.2212.03911" @default.
- W4310996987 hasPublicationYear "2022" @default.
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