PT  - JOURNAL ARTICLE
AU  - Mehdi Ali
AU  - Charles Tapley Hoyt
AU  - Daniel Domingo-Fernández
AU  - Jens Lehmann
AU  - Hajira Jabeen
TI  - BioKEEN: A library for learning and evaluating biological knowledge graph embeddings
AID  - 10.1101/475202
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 475202
4099  - http://biorxiv.org/content/early/2018/11/23/475202.short
4100  - http://biorxiv.org/content/early/2018/11/23/475202.full
AB  - 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 programming 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.Availability BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN as well as through PyPI.