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Related Experiment Video

Updated: Dec 28, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Biological applications of knowledge graph embedding models.

Sameh K Mohamed1, Aayah Nounu2, Vít Nováček3

  • 1Data Science Institute, NUI Galway, Galway, Irelands.kamal1@nuigalway.ie.

Briefings in Bioinformatics
|February 18, 2020
PubMed
Summary

Knowledge graph embedding (KGE) models offer a scalable solution for analyzing complex biological systems. These models represent biological knowledge graphs as vectors, improving prediction accuracy for tasks like drug-target interactions.

Keywords:
biomedical knowledge graphsdrug–target interactionsknowledge graph embeddingslink predictionpolypharmacy side effectstensor factorization

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Biological systems are traditionally modeled as graphs, analyzed using graph exploratory methods.
  • Existing methods face scalability limitations due to time-consuming path exploration.

Purpose of the Study:

  • To investigate knowledge graph embedding (KGE) models for biological knowledge graphs.
  • To demonstrate the applicability and advantages of KGEs in biological data analysis.

Main Methods:

  • Utilizing knowledge graph embedding (KGE) models to learn low-rank vector representations of biological graph nodes and edges.
  • Applying KGE models to analyze biological knowledge graphs and predict interactions.

Main Results:

  • KGE models show superior performance and accuracy compared to traditional graph exploratory approaches.
  • Case studies demonstrate KGE capabilities in predicting drug-target interactions and polypharmacy side effects.

Conclusions:

  • KGE models are a natural fit for representing complex biological knowledge.
  • KGEs offer enhanced predictive and analytical capabilities for biological systems, with practical considerations, opportunities, and challenges identified.