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

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  • 1Institute of Artificial Intelligence, Medical University of Vienna, 1090 Vienna, Austria.

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Summary

LinkExplorer predicts and explains links in biomedical knowledge graphs using the SAFRAN engine. This software offers an intuitive interface for exploring link predictions and their underlying reasoning, aiding hypothesis generation.

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Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Bioinformatics

Background:

  • Link prediction in biomedical knowledge graphs aids hypothesis generation.
  • Current machine learning algorithms often lack explainability and user-friendly interfaces.
  • Understanding prediction rationale is crucial for scientific insight.

Purpose of the Study:

  • To present LinkExplorer, a software suite for predicting, explaining, and exploring links in large biomedical knowledge graphs.
  • To integrate the novel, rule-based link prediction engine SAFRAN.
  • To provide an intuitive web interface for interactive exploration of link predictions and explanations.

Main Methods:

  • Utilized the SAFRAN (Scalable, Accurate, and Explainable Rule-based Association Network) engine for link prediction.
  • Evaluated the algorithm's performance on multiple large-scale biomedical knowledge graphs.
  • Developed a web interface for user interaction and visualization.

Main Results:

  • Demonstrated highly competitive evaluation results for the SAFRAN algorithm.
  • SAFRAN outperformed other explainable and established black-box algorithms in previous studies.
  • The web interface facilitates intuitive exploration of predicted links and their explanations.

Conclusions:

  • LinkExplorer provides a valuable tool for hypothesis generation in biomedical research.
  • The integration of SAFRAN enhances explainability in link prediction.
  • The interactive interface promotes deeper understanding and exploration of complex biological networks.