10. Graphlime: Local interpretable model explanations for graph neural networks 11. Graph neural networks in network neuroscience 12. Self-supervised learning of graph neural networks: A unified review 13. Protgnn: Towards self-explaining graph neural networks 14. Graph neural networks for recommender...
Graph Neural Networks in Network Neuroscience[M/OL]. arXiv, 2022[2023-06-29]. http://arxiv.org/abs/2106.03535. [8]吴博, 梁循, 张树森,等. 图神经网络前沿进展与应用[J]. 计算机学报, 2022, 45(1): 35-68. 作者:光影 / 范存源排版:光影| 封面:Tatiana Plakhova...
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is st...
We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell–cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated ...
5.5. Network Science and Neuroscience Connections 6. Related Work 7. Discussions 8. Conclusion Acknowledgments 《Graph Neural Networks: A Review of Methods and Applications》翻译与解读 ...
Graph Neural Networks in Network Neuroscience IEEE TPAMI 2022 - The toy example of different types of brain graph generated Prediction-Based Static Brain Graph Learning Paper TitleVenueYearModelCode EEG-Based Graph Neural Network Classification of Alzheimer’s Disease: An Empirical Evaluation of Functiona...
show that well-established graph techniques and methodologies offered in other science disciplines (network science, neuroscience, etc.) could contribute to understanding and designing deep neural networks. We believe this could be a fruitful avenue of future research that tackles more complex situations...
In conclusion, integrating brain networks with GNN offers a new avenue to study brain lesions using computational neuroscience and computer vision approaches.Wei, YiranLi, YonghaoChen, XiSchnlieb, Carola-BibianeLi, ChaoPrice, Stephen J.
Hence, they cannot effectively infer the active biological networks of diverse cell types simultaneously with cell clustering and have limited power to elucidate the response of these complex networks to external stimuli in specific cell types. Recently, graph neural networks (GNN) have shown strength...
Understanding the network organization of the brain has been a long-standing challenge for neuroscience. In the past decade, developments in graph theory have provided many new methods for topologically analysing complex networks, some of which have already been translated to the characterization of ana...