Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Pfeifer, B; Saranti, A; Holzinger, A.
GNN-SubNet: disease subnetwork detection with explainable graph neural networks.
Bioinformatics. 2022; 38(Supplement_2): ii120-ii126.
Doi: 10.1093/bioinformatics/btac478
Web of Science
PubMed
FullText
FullText_MUG
- Führende Autor*innen der Med Uni Graz
- Pfeifer Bastian
- Co-Autor*innen der Med Uni Graz
- Holzinger Andreas
- Saranti Anna
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- Abstract:
- MOTIVATION: The tremendous success of graphical neural networks (GNNs) already had a major impact on systems biology research. For example, GNNs are currently being used for drug target recognition in protein-drug interaction networks, as well as for cancer gene discovery and more. Important aspects whose practical relevance is often underestimated are comprehensibility, interpretability and explainability. RESULTS: In this work, we present a novel graph-based deep learning framework for disease subnetwork detection via explainable GNNs. Each patient is represented by the topology of a protein-protein interaction (PPI) network, and the nodes are enriched with multi-omics features from gene expression and DNA methylation. In addition, we propose a modification of the GNNexplainer that provides model-wide explanations for improved disease subnetwork detection. AVAILABILITY AND IMPLEMENTATION: The proposed methods and tools are implemented in the GNN-SubNet Python package, which we have made available on our GitHub for the international research community (https://github.com/pievos101/GNN-SubNet). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.