Biological Applications of Knowledge Graph Embedding Models

Sameh K Mohamed*, Aayah G Nounu, Vít Nováček

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

Abstract

Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph’s inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug–target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems.
Original languageEnglish
Article numberbbaa012
JournalBriefings in Bioinformatics
Early online date17 Feb 2020
DOIs
Publication statusE-pub ahead of print - 17 Feb 2020

Keywords

  • biomedical knowledge graphs
  • knowledge graph embeddings
  • tensor factorization
  • link prediction
  • drug–target interactions
  • polypharmacy side effects

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