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LinkExplorer: Predicting, explaining and exploring links in large biomedical knowledge graphs
[article]
2022
bioRxiv
pre-print
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
doi:10.1101/2022.01.09.475537
fatcat:jjw2naog2bcrxa3sza3utjmjza