• BioKEEN: a library for learnin...

Treffer: BioKEEN: a library for learning and evaluating biological knowledge graph embeddings.

Title:
BioKEEN: a library for learning and evaluating biological knowledge graph embeddings.
Authors:
Ali M; Department of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany., Hoyt CT; Department of Life Science Informatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.; Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany., Domingo-Fernández D; Department of Life Science Informatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.; Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany., Lehmann J; Department of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.; Department of Enterprise Information Systems, Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany., Jabeen H; Department of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2019 Sep 15; Vol. 35 (18), pp. 3538-3540.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Pattern Recognition, Automated* , Software*, Ecosystem ; Gene Library ; Machine Learning
Entry Date(s):
Date Created: 20190216 Date Completed: 20200610 Latest Revision: 20200610
Update Code:
20250114
DOI:
10.1093/bioinformatics/btz117
PMID:
30768158
Database:
MEDLINE

Weitere Informationen

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.
(© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)