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 ...

People are increasingly turning to AI for answers, and publishers are scrambling to find ways to consistently be surfaced in ...

Many datasets in various machine learning applications are structural and naturally represented as graphs. They comprise data from the analyses of social and communication networks, predictions of ...