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Hausleitner, C; Mueller, H; Holzinger, A; Pfeifer, B.
Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop.
Sci Rep. 2024; 14(1): 21839 Doi: 10.1038/s41598-024-72748-7 [OPEN ACCESS]
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Leading authors Med Uni Graz: Hausleitner Christian Matthias; Holzinger Andreas
Co-authors Med Uni Graz: Müller Heimo; Pfeifer Bastian
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Abstract:: The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechanisms on subgraphs within a Protein-Protein Interaction (PPI) network, situated in a federated ensemble-based deep learning context. This methodological approach marks a significant stride in the development of explainable and privacy-aware Artificial Intelligence, significantly contributing to the progression of personalized digital medicine in a responsible and transparent manner.
Find related publications in this database (using NLM MeSH Indexing): Humans - administration & dosage; Neural Networks, Computer - administration & dosage; Deep Learning - administration & dosage; Protein Interaction Maps - administration & dosage; Algorithms - administration & dosage; Artificial Intelligence - administration & dosage