Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

BioKEEN: a library for learning and evaluating biological knowledge graph embeddings

: Ali, M.; Hoyt, C.T.; Domingo-Fernandez, D.; Lehmann, J.; Jabeen, H.


Bioinformatics 35 (2019), No.18, pp.3538-3540
ISSN: 1367-4803
ISSN: 1460-2059
Journal Article
Fraunhofer SCAI ()
Fraunhofer IAIS ()

Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.