Exploring causal learning through graph neural networks: an in-depth review
In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …
Recognizing causal relationships embedded within data is pivotal for a comprehensive …
A systematic review of graph neural network in healthcare-based applications: Recent advances, trends, and future directions
Graph neural network (GNN) is a formidable deep learning framework that enables the
analysis and modeling of intricate relationships present in data structured as graphs. In …
analysis and modeling of intricate relationships present in data structured as graphs. In …
A survey of out‐of‐distribution generalization for graph machine learning from a causal view
J Ma - AI Magazine, 2024 - Wiley Online Library
Graph machine learning (GML) has been successfully applied across a wide range of tasks.
Nonetheless, GML faces significant challenges in generalizing over out‐of‐distribution …
Nonetheless, GML faces significant challenges in generalizing over out‐of‐distribution …
Brainib: Interpretable brain network-based psychiatric diagnosis with graph information bottleneck
Developing new diagnostic models based on the underlying biological mechanisms rather
than subjective symptoms for psychiatric disorders is an emerging consensus. Recently …
than subjective symptoms for psychiatric disorders is an emerging consensus. Recently …
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for
capturing complex dependencies within diverse graph-structured data. Despite their …
capturing complex dependencies within diverse graph-structured data. Despite their …
Riemannian manifold-based disentangled representation learning for multi-site functional connectivity analysis
Functional connectivity (FC), derived from resting-state functional magnetic resonance
imaging (rs-fMRI), has been widely used to characterize brain abnormalities in disorders. FC …
imaging (rs-fMRI), has been widely used to characterize brain abnormalities in disorders. FC …
Fusion of generative adversarial networks and non-negative tensor decomposition for depression fMRI data analysis
F Wang, H Ke, Y Tang - Information Processing & Management, 2025 - Elsevier
Objective: This study introduces a novel approach, F-GAN-NTD, which integrates Generative
Adversarial Networks (GANs) with Non-negative Tensor Decomposition (NTD) theory to …
Adversarial Networks (GANs) with Non-negative Tensor Decomposition (NTD) theory to …
An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network
S Liu, J Zhou, X Zhu, Y Zhang, X Zhou, S Zhang… - Patterns, 2024 - cell.com
This study developed an artificial intelligence (AI) system using a local-global multimodal
fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major …
fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major …
[HTML][HTML] Exploring the impact of APOE ɛ4 on functional connectivity in Alzheimer's disease across cognitive impairment levels
The apolipoprotein E (APOE) ɛ 4 allele is a recognized genetic risk factor for Alzheimer's
Disease (AD). Studies have shown that APOE ɛ 4 mediates the modulation of intrinsic …
Disease (AD). Studies have shown that APOE ɛ 4 mediates the modulation of intrinsic …
Graph neural network with modular attention for identifying brain disorders
W Si, G Wang, L Liu, L Zhang, L Qiao - Biomedical Signal Processing and …, 2025 - Elsevier
Abstract Functional Magnetic Resonance Imaging (fMRI), by detecting the cerebral Blood
Oxygen Level-Dependent (BOLD) signals, has developed into an effective technique to aid …
Oxygen Level-Dependent (BOLD) signals, has developed into an effective technique to aid …