For rare diseases, AI-driven repurposing fills a critical gap. With more than 7000 rare diseases and only a small percentage ...
Understanding how the brain learns and applies rules is the key to unraveling the neural basis of flexible behavior. A new ...
Frontiers in Neurorobotics has published a new paper by Xiaofei Han and Xin Dou introducing a next-generation artificial intelligence framework that combines graph neural networks and multimodal ...
Abstract: Graph Convolutional Networks (GCNs) prevail in the analysis of network-structured data, but how the graph size affects the performance is not fully understood. As the limit of graphs, the ...
Despite differing regulatory philosophies, all regions recognize AI’s value in transactional transparency, anomaly detection, and early fraud prevention. The study identifies a clear shift from manual ...
Recurrent spontaneous abortion (RSA) is a complex, multifactorial condition that presents significant diagnostic challenges.
Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances
Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
1 Business College, California State University, Long Beach, CA, United States 2 School of Business and Management, Shanghai International Studies University, Shanghai, China In common graph neural ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. In ...
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