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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Knowledge graph embedding for predicting and analyzing microbial interactions.

Mohammed Khatbane1,2, Cécile Mangavel2, Frédéric Borges2

  • 1Université de Lorraine, CNRS, LORIA, 54000, Nancy, France.

Scientific Reports
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

Predicting microbial interactions is crucial for ecology. A new knowledge graph embedding framework accurately forecasts microbial partnerships, reducing the need for extensive lab experiments and enabling microbial community engineering.

Keywords:
Knowledge graph embeddingMachine learningMicrobial ecologyMicrobial interactions

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Area of Science:

  • Microbial Ecology
  • Computational Biology
  • Bioinformatics

Background:

  • Microbial interactions are key to community structure and function.
  • Predicting these interactions is challenging.
  • Current machine learning methods require extensive experimental data.

Purpose of the Study:

  • To develop a novel framework for predicting pairwise microbial interactions.
  • To minimize the need for in vitro experimentation.
  • To leverage knowledge graph embedding for microbial interaction prediction.

Main Methods:

  • Knowledge graph embedding to represent microorganisms and their interactions.
  • Utilizing a dataset of 20 soil bacterial strains across 40 carbon source environments.
  • Developing a model to predict pairwise interactions, including those with missing data.

Main Results:

  • The framework accurately predicts pairwise microbial interactions.
  • The model successfully predicts interactions for strains with incomplete experimental data.
  • Learned embeddings reveal similarities between carbon source environments, enabling cross-environment predictions.
  • The approach facilitates the design of a recommendation system for microbial community engineering.

Conclusions:

  • Knowledge graph embedding is a powerful and promising strategy for microbial ecology.
  • The proposed framework reduces experimental burden and enhances predictive accuracy.
  • This method supports advancements in microbial community engineering and ecological studies.