Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

Pfeifer, B.; Chereda, H.; Martin, R.; Saranti, A.; Angerschmid, A.; Clemens, S.; Hauschild, A.-C.; Beissbarth, T.; Holzinger, A.; Heider, D.

doi:10.1101/2023.03.22.533772

bioRxiv · 10.1101/2023.03.22.533772

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

Pfeifer, B.·Chereda, H.·Martin, R.·Saranti, A.·Angerschmid, A.·Clemens, S.·Hauschild, A.-C.·Beissbarth, T.·Holzinger, A.·Heider, D.

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Abstract

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).

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Pfeifer, B., Chereda, H., Martin, R., Saranti, A., Angerschmid, A., Clemens, S., Hauschild, A.-C., Beissbarth, T., Holzinger, A., Heider, D.. 2023-03-24. Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification. https://doi.org/10.1101/2023.03.22.533772

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