SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
%0 Journal Article
%1 Ali2019-oz
%A Ali, Mehdi
%A Hoyt, Charles Tapley
%A Domingo-Fernández, Daniel
%A Lehmann, Jens
%A Jabeen, Hajira
%D 2019
%J Bioinformatics
%K
%N 18
%P 3538--3540
%T BioKEEN: a library for learning and evaluating biological knowledge graph embeddings
%V 35
%X SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
@article{Ali2019-oz,
abstract = {SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Ali, Mehdi and Hoyt, Charles Tapley and Domingo-Fern{\'a}ndez, Daniel and Lehmann, Jens and Jabeen, Hajira},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/21df28d549b46e9247322402e4435bccd/scadsfct},
interhash = {d6fb71a3e2bd6293e9640269aed3ad0a},
intrahash = {1df28d549b46e9247322402e4435bccd},
journal = {Bioinformatics},
keywords = {},
language = {en},
month = sep,
number = 18,
pages = {3538--3540},
timestamp = {2024-09-10T15:15:57.000+0200},
title = {{BioKEEN}: a library for learning and evaluating biological knowledge graph embeddings},
volume = 35,
year = 2019
}
