ArticleBioinformatics (Oxford, England)2023
Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Natural Products in Epilepsy Treatment: From Traditional Medicine Towards Computational Drug Discovery.Current issues in molecular biology · 2026Review
- Gait Recognition via Enhanced Visual-Audio Ensemble Learning with Decision Support Methods.Sensors (Basel, Switzerland) · 2025Article
- gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes.Computational and structural biotechnology journal · 2025Article
- Privacy-preserving decentralized learning methods for biomedical applications.Computational and structural biotechnology journal · 2024Review
- GNN4DM: a graph neural network-based method to identify overlapping functional disease modules.Bioinformatics (Oxford, England) · 2024Article
- Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop.Scientific reports · 2024Article
- Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network.Digital healthArticle
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Authors and funding
9 authors.
Funding
Abstract
summaryFederated 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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.