LinkExplorer: Predicting, explaining and exploring links in large biomedical knowledge graphs [article]

Simon Ott, Adriano Barbosa-Silva, Matthias Samwald
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
more » ... orithms 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.
doi:10.1101/2022.01.09.475537 fatcat:jjw2naog2bcrxa3sza3utjmjza