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

View ORCID ProfileMehdi Ali, View ORCID ProfileCharles Tapley Hoyt, View ORCID ProfileDaniel Domingo-Fernández, View ORCID ProfileJens Lehmann, Hajira Jabeen
doi: https://doi.org/10.1101/475202
Mehdi Ali
1Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany
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Charles Tapley Hoyt
1Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany
2Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin 53754, Germany
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Daniel Domingo-Fernández
1Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany
2Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin 53754, Germany
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Jens Lehmann
1Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany
3Department of Enterprise Information Systems, Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin 53754, Germany
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Hajira Jabeen
1Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany
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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 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 23, 2018.
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BioKEEN: A library for learning and evaluating biological knowledge graph embeddings
Mehdi Ali, Charles Tapley Hoyt, Daniel Domingo-Fernández, Jens Lehmann, Hajira Jabeen
bioRxiv 475202; doi: https://doi.org/10.1101/475202
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BioKEEN: A library for learning and evaluating biological knowledge graph embeddings
Mehdi Ali, Charles Tapley Hoyt, Daniel Domingo-Fernández, Jens Lehmann, Hajira Jabeen
bioRxiv 475202; doi: https://doi.org/10.1101/475202

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