BioKEEN: a library for learning and evaluating biological knowledge graph embeddings
Permanent link to this recordhttp://hdl.handle.net/10754/668066
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AbstractKnowledge 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.
CitationAli, M., Hoyt, C. T., Domingo-Fernández, D., Lehmann, J., & Jabeen, H. (2019). BioKEEN: a library for learning and evaluating biological knowledge graph embeddings. Bioinformatics, 35(18), 3538–3540. doi:10.1093/bioinformatics/btz117
SponsorsWe thank our partners from the Bio2Vec, MLwin and SimpleML projects for their assistance.
This research was supported by Bio2Vec project (http://bio2vec.net/, CRG6) grant  with funding from King Abdullah University of Science and Technology (KAUST).
PublisherOxford University Press (OUP)