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 ...
Frontiers

Exploring physical and functional EEG connectivity with multilayer graph transformer convolutional networks for emotion recognition

1 School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China 2 School of Engineering, University of Birmingham, Birmingham, United Kingdom Electroencephalogram ...
IEEE

Multigranularity Deep Graph Convolutional Neural Network Node Clustering Leveraging Spatial Information

Abstract: In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From ...
Frontiers

Multimodal graph neural networks in healthcare: a review of fusion strategies across biomedical domains

1 Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA, United States 2 Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States ...
Scientific Research Publishing

An In-Depth Analysis of Graph Neural Networks and Machine Learning Approaches for Drug Repositioning ()

1 Department of Computer Science and Engineering, Kishoreganj University, Kishoreganj, Bangladesh. 2 Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. 3 ...
C&EN

Graph Convolutional Neural Networks for Predicting Drug-Target Interactions

Deparment of Bioengineering, Stanford University, Stanford, California 94305, United States Department of Genetics, Stanford University, Stanford, California 94305, United States Accurate ...