Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease (2403.02786v1)
Abstract: Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from health checkup data. The effectiveness of various GNN approaches in this context is demonstrated, even with minimal labeled samples. Central to our methodology is the inclusion of human-centric explanations through explainable GNNs, providing personalized feature importance scores for enhanced interpretability and clinical relevance, thereby underscoring the potential of our approach in advancing healthcare practices with a keen focus on graph representation learning and human-centric explanation.
- Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits. Scientific Reports, 12(1): 1930.
- Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
- GRAND++: Graph neural diffusion with a source term. In International Conference on Learning Representation (ICLR).
- Graph attention networks. arXiv preprint arXiv:1710.10903.
- Similarity network fusion for aggregating data types on a genomic scale. Nature methods, 11(3): 333–337.
- Dissecting the diffusion process in linear graph convolutional networks. Advances in Neural Information Processing Systems, 34: 5758–5769.
- Difformer: Scalable (graph) transformers induced by energy constrained diffusion. arXiv preprint arXiv:2301.09474.
- Nodeformer: A scalable graph structure learning transformer for node classification. Advances in Neural Information Processing Systems, 35: 27387–27401.
- Graph representation learning in bioinformatics: trends, methods and applications. Briefings in Bioinformatics, 23(1): bbab340.
- Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems, 32.
- Graph neural networks and their current applications in bioinformatics. Frontiers in genetics, 12: 690049.
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