Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed ...

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
Scientific Research Publishing

Damage Detection in Simply Supported Beams Using 1D Convolutional Neural Networks with ...

1 Guangzhou Railway Polytechnic, Guangzhou, China. 2 School of Intelligent Construction and Civil Engineering, Zhongyuan University of Technology, Zhengzhou, China. 3 Research Center for Wind ...
GitHub

graph-convolutional-network

Add a description, image, and links to the graph-convolutional-network topic page so that developers can more easily learn about it.
C&EN

Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case ...

Department of Chemistry and Biochemistry, University of Wisconsin─Eau Claire, Eau Claire, Wisconsin 54702, United States ...
IEEE

Cross-Domain Explainable Recommendation Using Graph Convolutional Networks and a Topic Model

Abstract: Graph Convolutional Network (GCN)-based recommendation systems (RSs) have recently gained popularity for their ability to improve recommendation accuracy by utilizing neighborhood ...
IEEE

Implementing a Graph Convolutional Network (GCN) model for the early detection of Alzheimer ...

Abstract: This research presents a diagnosis model of single biomarker of Alzheimer’s diseases using graph convolutional network (GCN). Alzheimer’s disease is quite a challenging disease to diagnose ...
Frontiers

A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on ...

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare ...