Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, 8036, Graz, Austria.
Human-Centered AI Lab, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, 1190, Vienna, Austria.
Sci Rep. 2024 Sep 19;14(1):21839. doi: 10.1038/s41598-024-72748-7.
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.
作者提出了一种将联邦学习与图神经网络（GNN）相结合的新框架，以分类疾病，并结合了人机交互方法。该先进框架在联邦基于深度学习的环境中，创新性地采用了蛋白质-蛋白质相互作用（PPI）网络中子图上的协作投票机制。这种方法标志着可解释和隐私感知人工智能的发展迈出了重要一步，为负责任和透明的个性化数字医学的发展做出了重要贡献。
