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

Mehdi Ali, Charles Tapley Hoyt, Daniel Domingo-Fernández, Jens Lehmann, Hajira Jabeen

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.
Original languageEnglish (US)
Pages (from-to)3538-3540
Number of pages3
JournalBioinformatics
Volume35
Issue number18
DOIs
StatePublished - Feb 15 2019
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Molecular Biology
  • Statistics and Probability
  • Computer Science Applications

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