RT Journal Article
SR Electronic
T1 BioKEEN: A library for learning and evaluating biological knowledge graph embeddings
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 475202
DO 10.1101/475202
A1 Mehdi Ali
A1 Charles Tapley Hoyt
A1 Daniel Domingo-Fernández
A1 Jens Lehmann
A1 Hajira Jabeen
YR 2018
UL http://biorxiv.org/content/early/2018/11/23/475202.abstract
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.