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Ratajczak, F. ; Joblin, M.* ; Ringsquandl, M.* ; Hildebrandt, M.*

Task-driven knowledge graph filtering improves prioritizing drugs for repurposing.

BMC Bioinformatics 23:84 (2022)
Publ. Version/Full Text Research data DOI
Open Access Gold
Creative Commons Lizenzvertrag
BACKGROUND: Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. RESULTS: We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. CONCLUSION: Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Drug Repurposing ; Knowledge Graph Embeddings ; Knowledge Graphs ; Link Prediction
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Quellenangaben Volume: 23, Issue: 1, Pages: , Article Number: 84 Supplement: ,
Publisher BioMed Central
Publishing Place Campus, 4 Crinan St, London N1 9xw, England
Reviewing status Peer reviewed
Institute(s) Institute of Network Biology (INET)
Grants Helmholtz Association under the joint research school "Munich School for Data Science MUDS"
Siemens AG, Munich, Germany
Siemens Healthineers, Erlangen, Germany