Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.03.22.533772v1?rss=1
Authors: Pfeifer, B., Chereda, H., Martin, R., Saranti, A., Angerschmid, A., Clemens, S., Hauschild, A.-C., Beissbarth, T., Holzinger, A., Heider, D.
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, e.g., for protein-protein-interaction (PPI) networks. Here, we present our ensemble-GNN library, which can be used to build 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 exemplarily show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA).
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