LinkExplorer: predicting, explaining and exploring links in large biomedical knowledge graphs

Abstract Summary Machine learning algorithms for link prediction can be valuable tools for hypothesis generation. However, many current algorithms are black boxes or lack good user interfaces that could facilitate insight into why predictions are made. We present LinkExplorer, a software suite for p...

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Bibliographic Details
Published in:Bioinformatics 2022-04, Vol.38 (8), p.2371-2373
Main Authors: Ott, Simon, Barbosa-Silva, Adriano, Samwald, Matthias
Format: Article
Language:English
Subjects:
Algorithms
Documentation
Machine Learning
Pattern Recognition, Automated
Software
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Summary:Abstract Summary Machine learning algorithms for link prediction can be valuable tools for hypothesis generation. However, many current algorithms are black boxes or lack good user interfaces that could facilitate insight into why predictions are made. We present LinkExplorer, a software suite for predicting, explaining and exploring links in large biomedical knowledge graphs. LinkExplorer integrates our novel, rule-based link prediction engine SAFRAN, which was recently shown to outcompete other explainable algorithms and established black-box algorithms. Here, we demonstrate highly competitive evaluation results of our algorithm on multiple large biomedical knowledge graphs, and release a web interface that allows for interactive and intuitive exploration of predicted links and their explanations. Availability and implementation A publicly hosted instance, source code and further documentation can be found at https://github.com/OpenBioLink/Explorer. Supplementary information Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btac068