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SHR Neuro Cancer Cardio Lipid Metab Microb

Pfeifer, B; Chereda, H; Martin, R; Saranti, A; Clemens, S; Hauschild, AC; Beißbarth, T; Holzinger, A; Heider, D.
Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.
Bioinformatics. 2023; 39(11): Doi: 10.1093/bioinformatics/btad703 [OPEN ACCESS]
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Leading authors Med Uni Graz: Pfeifer Bastian
Co-authors Med Uni Graz: Holzinger Andreas; Saranti Anna
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Abstract:: SUMMARY: Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA). AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/pievos101/Ensemble-GNN, and the data at Zenodo (DOI: 10.5281/zenodo.8305122).
Find related publications in this database (using NLM MeSH Indexing): Humans - administration & dosage; DNA Methylation - administration & dosage; Machine Learning - administration & dosage; Neural Networks, Computer - administration & dosage; Protein Interaction Maps - administration & dosage; Software - administration & dosage