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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Disease gene classification with metagraph representations.

Sezin Kircali Ata1, Yuan Fang2, Min Wu2

  • 1Computer Science and Engineering, Nanyang Technological University, Singapore.

Methods (San Diego, Calif.)
|July 12, 2017
PubMed
Summary

We introduce a new method to predict disease proteins by integrating protein properties into protein-protein interaction networks. This novel approach enhances disease gene discovery and outperforms existing methods.

Keywords:
Disease protein predictionMetagraphProtein representationsProtein-protein interactionUniprot keywords

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Area of Science:

  • Bioinformatics
  • Network Biology
  • Computational Biology

Background:

  • Protein-protein interaction (PPI) networks are crucial for understanding protein functions and disease associations.
  • However, PPI networks alone lack sufficient biological context, necessitating integration with other knowledge sources.
  • Existing methods often fail to fully capture the complex relationships between proteins and their functions.

Purpose of the Study:

  • To develop a novel method for enriching PPI networks with biological properties of proteins.
  • To construct a new network model, the PPI-Keywords (PPIK) network, incorporating both proteins and descriptive keywords.
  • To improve disease protein prediction accuracy by leveraging topological features within the PPIK network.

Main Methods:

  • Integrated keywords describing protein properties into existing PPI networks to create PPIK networks.
  • Represented proteins using novel metagraphs that capture topological arrangements of proteins and keywords.
  • Employed supervised learning with classifiers built upon these metagraph representations for disease protein prediction.

Main Results:

  • The proposed method consistently improved disease protein prediction accuracy across multiple classifiers and PPI databases, with an average AUC increase of 15.3%.
  • Outperformed established baseline methods, including diffusion-based (e.g., RWR) and module-based approaches, by 13.8-32.9% for general disease protein prediction.
  • Demonstrated superior performance in predicting breast cancer genes, outperforming baselines by 6.6-14.2%, and showed better correlation with PubMed literature findings.

Conclusions:

  • The PPIK network and metagraph representation offer a powerful framework for integrating diverse biological information.
  • This approach significantly enhances the accuracy and reliability of disease protein prediction.
  • The method provides a valuable tool for advancing our understanding of disease mechanisms and identifying potential therapeutic targets.