BioKEEN: A library for learning and evaluating biological knowledge graph embeddings. Ali, M., Hoyt, C. T., Domingo-Fernandez, D., Lehmann, J., & Jabeen, H. bioRxiv, November, 2018. ![BioKEEN: A library for learning and evaluating biological knowledge graph embeddings [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{ali_biokeen:_2018,
title = {{BioKEEN}: {A} library for learning and evaluating biological knowledge graph embeddings},
copyright = {© 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NoDerivs 4.0 International), CC BY-ND 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/},
shorttitle = {{BioKEEN}},
url = {https://www.biorxiv.org/content/early/2018/11/23/475202},
doi = {10.1101/475202},
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.},
language = {en},
urldate = {2018-11-23},
journal = {bioRxiv},
author = {Ali, Mehdi and Hoyt, Charles Tapley and Domingo-Fernandez, Daniel and Lehmann, Jens and Jabeen, Hajira},
month = nov,
year = {2018},
pages = {475202},
}
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