As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Discover the leading database management systems for enterprises in 2026. Explore key features, pricing, and implementation tips for selecting the best DBMS software to harness your data effectively.

Structure Therapeutics’ stock soared toward an 18-month high in early Monday trading, after mid-stage data for its oral daily GLP-1 receptor appeared comparable to that of one of Eli Lilly’s ...

Dr. Slotkin is a neurosurgeon. I recently got called to see a teenager ejected in a rollover car crash. The trauma team rushed him into surgery to stop major abdominal bleeding, but we all knew. When ...

Abstract: Graph neural network is a new neural network model in recent years, whose advantage lies in processing graph structure data. In the era of big data, people can collect a large amount of ...

A TechRadar article noted that nearly 90% of enterprise information (documents, emails, videos) lies dormant in unstructured systems. This "dark data" isn't just neglected; it's a liability. GenAI ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...

Powerful as they are, graph neural networks (GNNs) are known to be vulnerable to distribution shifts. Recently, test-time adaptation (TTA) has attracted attention due to its ability to adapt a ...

1 School of Biomedical Engineering, Tianjin Medical University, Tianjin, China 2 State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang ...

Abstract: In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating ...