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

Abstract
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.

Citation
Ali, 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

Acknowledgements
We 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 [3454] with funding from King Abdullah University of Science and Technology (KAUST).

Publisher
Oxford University Press (OUP)

Journal
Bioinformatics

DOI
10.1093/bioinformatics/btz117

Additional Links
https://academic.oup.com/bioinformatics/article/35/18/3538/5320556https://www.biorxiv.org/content/biorxiv/early/2018/11/23/475202.full.pdf

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