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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A step towards quantifying, modelling and exploring uncertainty in biomedical knowledge graphs.

Adil Bahaj1, Mounir Ghogho2

  • 1International University of Rabat, TICLab, Sala el Jadida 11103, Morocco.

Computers in Biology and Medicine
|November 14, 2024
PubMed
Summary

This study quantifies uncertainty in biomedical knowledge graphs (BKGs) using deep learning on textual evidence. The developed method, KGB2U, enables automated uncertainty assessment and knowledge discovery from large BKGs.

Keywords:
Biomedical knowledge graphsKnowledge graph embeddingPrecision medicineUncertain knowledge graphs

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Biomedical knowledge graphs (BKGs) are crucial for organizing complex biological data.
  • Quantifying the uncertainty of facts within BKGs is essential for reliable data interpretation.
  • Existing methods often rely on manual feature engineering, limiting scalability and accuracy.

Purpose of the Study:

  • To develop a deep learning approach for automatic quantification and modeling of uncertainty in BKGs.
  • To leverage textual supporting evidence to assess the factuality and confidence scores of BKG entries.
  • To enable knowledge discovery and identify novel insights from uncertain BKG data.

Main Methods:

  • Utilized a sentence transformer for deep feature extraction from supporting sentences.
  • Employed a naive Bayes classifier to determine sentence factuality.
  • Quantified fact uncertainty by averaging sentence factuality scores, producing confidence values between 0 and 1.

Main Results:

  • The deep learning model significantly outperformed traditional methods using hand-crafted features.
  • Demonstrated the capability to process large-scale BKGs, creating a new uncertain BKG dataset (USemMedDB) from SemMedDB.
  • Showcased the correlation between BKG structure and confidence scores, and the model's ability to predict confidence for new facts.

Conclusions:

  • Textual evidence can be effectively used to automatically quantify uncertainty in BKG facts.
  • The developed uncertain BKGs facilitate knowledge discovery and identification of novel scientific insights.
  • The KGB2U tool is available for processing and analyzing large biomedical knowledge graphs.