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

Mehdi Ali1, Charles Tapley Hoyt2,3, Daniel Domingo-Fernández2,3

  • 1Department of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.

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BioKEEN and PyKEEN offer accessible tools for biological knowledge graph embeddings, simplifying complex machine learning for bioinformatics. These packages enable link prediction for biological pathways, making advanced analysis more widely available.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Knowledge graph embeddings (KGEs) are powerful for link prediction and data representation in various fields.
  • The bioinformatics domain lacks accessible software for KGE applications, hindering broader adoption.
  • Existing tools require significant programming and machine learning expertise.

Purpose of the Study:

  • To develop user-friendly software for applying KGEs in bioinformatics.
  • To make KGEs accessible to researchers without extensive programming or machine learning backgrounds.
  • To demonstrate the utility of KGEs for biological pathway analysis.

Main Methods:

  • Development of BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) packages.
  • Implementation of an interactive command-line interface for ease of use.
  • Application of KGEs to a novel biological pathway mapping resource.

Main Results:

  • BioKEEN and PyKEEN provide accessible KGE tools for bioinformatics.
  • The developed packages facilitate link prediction for biological pathway crosstalks and hierarchies.
  • A case study demonstrates successful application in pathway analysis.

Conclusions:

  • BioKEEN and PyKEEN lower the barrier to entry for KGEs in biological research.
  • These tools enable novel insights into biological pathway relationships.
  • The software promotes wider use of advanced computational methods in bioinformatics.