By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

Graph convolutional networks (GCNs) emerge as the most successful learning models for graph-structured data. Despite their success, existing GCNs usually ignore the entangled latent factors typically ...

GREmLN leverages a graph-based architecture to represent gene-gene interactions to predict cell behavior for therapeutic ...

o3, an artificial intelligence (AI) model developed by the creators of ChatGPT, has been ranked the best AI tool for ...

The Graph, the open, universal data layer for web3, announced today a strategic integration with the TRON blockchain network ...

Objectives (1) To develop an understanding of how social capital may be conceptualised within the context of end-of-life care and how it can influence outcomes for people with dementia and their ...

Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or ...

Deadline reports Syracuse University alumnus Aaron Sorkin has written a sequel to the Oscar-winning 2010 film, “The Social Network.” Sorkin will also direct the new movie, currently titled ...

PyTorch Geometric implementations of GraphSAGE and GAT (Graph Attention Networks) for node classification on citation networks.