CC BY 4.0Ali, MehdiMehdiAliHoyt, Charles TapleyCharles TapleyHoytDomingo-Fernandez, DanielDanielDomingo-FernandezLehmann, JensJensLehmann2022-03-1412.11.20192019https://publica.fraunhofer.de/handle/publica/40553510.24406/publica-fhg-405535PyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert.enKnowledge Graphsbioinformaticnetwork representation learning005003005006518006629conference paper