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We developed a novel explainable graph neural network (GNN) framework, GNN-SubNet, for disease subnetwork detection using multi-omics data and protein-protein interaction networks. This tool enhances interpretability in systems biology research.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Graphical neural networks (GNNs) have shown significant success in systems biology, aiding in tasks like drug target recognition and cancer gene discovery.
  • Interpretability, explainability, and comprehensibility are crucial yet often underestimated aspects of GNN applications in biological research.

Purpose of the Study:

  • To introduce a novel graph-based deep learning framework for disease subnetwork detection.
  • To enhance the interpretability and explainability of GNNs in biological data analysis.

Main Methods:

  • Developed a graph-based deep learning framework representing patients via protein-protein interaction (PPI) network topology.
  • Integrated multi-omics features (gene expression, DNA methylation) into network nodes.
  • Modified GNNexplainer to provide model-wide explanations for improved disease subnetwork detection.

Main Results:

  • Successfully implemented a novel framework for disease subnetwork detection using explainable GNNs.
  • Achieved improved disease subnetwork detection through model-wide explanations.
  • Demonstrated the utility of integrating multi-omics data with PPI networks.

Conclusions:

  • The proposed framework offers a powerful and interpretable approach to disease subnetwork detection.
  • The GNN-SubNet package facilitates the application of explainable GNNs in systems biology.
  • This work contributes to advancing the understanding of complex diseases through computational methods.