AIMC Topic: Graph Neural Networks

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A lightweight graph neural network to predict long-term mortality in coronary artery disease patients: an interpretable causality-aware approach.

Journal of biomedical informatics Jul 1, 2025
BACKGROUND: Coronary artery disease (CAD) causes substantial death toll in the United States and worldwide. While traditional methods for CAD mortality prediction are based on established risk factors, they have significant limitations in accuracy, a...
Male Middle Aged Risk Factors Aged Coronary Artery Disease Graph Neural Networks Risk Assessment Logistic Models Humans Machine Learning Neural Networks, Computer Female
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Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data.

Artificial intelligence in medicine Jul 1, 2025
In healthcare, predictive analysis using unstructured medical data is crucial for gaining insights into patient conditions and outcomes. However, unstructured data, which contains valuable patient information such as symptoms and medical histories, o...
Hospital Mortality Graph Neural Networks Electronic Health Records Length of Stay Intensive Care Units Humans Neural Networks, Computer Machine Learning
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Mathematical expression exploration with graph representation and generative graph neural network.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Symbolic Regression (SR) methods in tree representations have exhibited commendable outcomes across Genetic Programming (GP) and deep learning search paradigms. Nonetheless, the tree representation of mathematical expressions occasionally embodies re...
Deep Learning Neural Networks, Computer Humans Graph Neural Networks Algorithms
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Motif and supernode-enhanced gated graph neural networks for session-based recommendation.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Session-based recommendation systems aim to predict users' next interactions based on short-lived, anonymous sessions, a challenging yet vital task due to the sparsity and dynamic nature of user behavior. Existing Graph Neural Network (GNN)-based met...
Humans Algorithms Neural Networks, Computer Graph Neural Networks
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SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (...
Neural Networks, Computer Algorithms Computer Simulation Graph Neural Networks Molecular Dynamics Simulation
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Adaptive node-level weighted learning for directed graph neural network.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Directed graph neural networks (DGNNs) have garnered increasing interest, yet few studies have focused on node-level representation in directed graphs. In this paper, we argue that different nodes rely on neighbor information from different direction...
Graph Neural Networks Algorithms Learning Neural Networks, Computer Machine Learning Humans
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Graph Neural Networks with Coarse- and Fine-Grained Division for mitigating label noise and sparsity.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Graph Neural Networks (GNNs) have gained considerable prominence in semi-supervised learning tasks in processing graph-structured data, primarily owing to their message-passing mechanism, which largely relies on the availability of clean labels. Howe...
Humans Neural Networks, Computer Algorithms Graph Neural Networks
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Heterogeneous Graph Neural Network with Adaptive Relation Reconstruction.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Topological structures of real-world graphs often exhibit heterogeneity involving diverse nodes and relation types. In recent years, heterogeneous graph learning methods utilizing meta-paths to capture composite relations and guide neighbor selection...
Neural Networks, Computer Graph Neural Networks Humans Algorithms
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ENsiRNA: A Multimodality Method for siRNA-mRNA and Modified siRNA Efficacy Prediction Based on Geometric Graph Neural Network.

Journal of molecular biology Jun 15, 2025
With the rise of small interfering RNA (siRNA) as a therapeutic tool, effective siRNA design is crucial. Current methods often emphasize sequence-related features, overlooking structural information. To address this, we introduce ENsiRNA, a multimoda...
Neural Networks, Computer Computational Biology Humans Thermodynamics Graph Neural Networks RNA, Messenger RNA, Small Interfering
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Improved prediction of chlorophyll-a concentrations using advancing graph neural network variants.

The Science of the total environment Jun 1, 2025
Accurate estimation of harmful algal blooms is essential for protecting surface water. Chlorophyll-a (Chl-a), commonly used as a proxy for estimating algal concentration, is influenced by a broad range of weather and physicochemical factors that oper...
Harmful Algal Bloom Chlorophyll A Environmental Monitoring Water Quality Graph Neural Networks Neural Networks, Computer Republic of Korea
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