ascopubs.org

Predicting non-coding RNA-based regulatory networks in cancer metastasis using a heterogeneous hierarchical graph transformer.

Enhancing clinical trial imaging workflow with AI integration. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not include a full text component.
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

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 ...
VentureBeat

LinkedIn creates PASS to tailor graph neural networks for social media

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Graphs are, quite simply, a universal ...
Finextra

Graph Neural Networks and GraphRAG: Navigating Open-World Complexity in Finance

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...