{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:38:10Z","timestamp":1774946290022,"version":"3.50.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":20,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["826078"],"award-info":[{"award-number":["826078"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>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\u2013protein 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code is available at https:\/\/github.com\/pievos101\/Ensemble-GNN, and the data at Zenodo (DOI: 10.5281\/zenodo.8305122).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad703","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T13:19:24Z","timestamp":1700486364000},"source":"Crossref","is-referenced-by-count":22,"title":["Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7035-9535","authenticated-orcid":false,"given":"Bastian","family":"Pfeifer","sequence":"first","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Documentation, Medical University Graz , Graz 8036, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1103-0136","authenticated-orcid":false,"given":"Hryhorii","family":"Chereda","sequence":"additional","affiliation":[{"name":"Medical Bioinformatics, University Medical Center G\u00f6ttingen , G\u00f6ttingen 37077, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7678-7856","authenticated-orcid":false,"given":"Roman","family":"Martin","sequence":"additional","affiliation":[{"name":"Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg , Marburg 35043, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1085-8428","authenticated-orcid":false,"given":"Anna","family":"Saranti","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Documentation, Medical University Graz , Graz 8036, Austria"},{"name":"Human-Centered AI Lab, University of Natural Resources and Life Sciences , Vienna 1190, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9710-1152","authenticated-orcid":false,"given":"Sandra","family":"Clemens","sequence":"additional","affiliation":[{"name":"Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg , Marburg 35043, Germany"}]},{"given":"Anne-Christin","family":"Hauschild","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, University Medical Center G\u00f6ttingen , G\u00f6ttingen 37075, Germany"}]},{"given":"Tim","family":"Bei\u00dfbarth","sequence":"additional","affiliation":[{"name":"Medical Bioinformatics, University Medical Center G\u00f6ttingen , G\u00f6ttingen 37077, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6786-5194","authenticated-orcid":false,"given":"Andreas","family":"Holzinger","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Documentation, Medical University Graz , Graz 8036, Austria"},{"name":"Human-Centered AI Lab, University of Natural Resources and Life Sciences , Vienna 1190, Austria"}]},{"given":"Dominik","family":"Heider","sequence":"additional","affiliation":[{"name":"Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg , Marburg 35043, Germany"}]}],"member":"286","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"2023112902093877400_btad703-B1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.csbj.2022.11.050","article-title":"A guide to multi-omics data collection and integration for translational 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