AIMC Topic: Graph Neural Networks

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Machine learning approaches for predicting craniofacial anomalies with graph neural networks.

Computational biology and chemistry Dec 2, 2024
This study explores the use of machine learning algorithms, including traditional approaches and graph neural networks (GNNs), to predict certain diseases by analyzing protein-protein interactions. Protein-protein interactions (PPIs) are complex, mul...
Craniofacial Abnormalities Graph Neural Networks Neural Networks, Computer Humans Machine Learning Protein Interaction Maps
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Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis.

IEEE transactions on neural networks and learning systems Dec 2, 2024
The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces (BCIs) based on electroencephalography (EEG). Over the past few decades, the performance of MI-EEG classifiers has seen gradual enhancement. In thi...
Graph Neural Networks Time Factors Humans Brain-Computer Interfaces Deep Learning Neural Networks, Computer Imagination Electroencephalography Signal Processing, Computer-Assisted Algorithms
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Regional PM prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors.

Environmental pollution (Barking, Essex : 1987) Nov 28, 2024
Traditional statistical prediction methods on PM often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed gra...
Particulate Matter Forecasting Seasons Spatio-Temporal Analysis Cities Neural Networks, Computer Meteorological Concepts China Environmental Monitoring Graph Neural Networks Air Pollutants Air Pollution
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Graph Batch Coarsening framework for scalable graph neural networks.

Neural networks : the official journal of the International Neural Network Society Nov 22, 2024
Due to the neighborhood explosion phenomenon, scaling up graph neural networks to large graphs remains a huge challenge. Various sampling-based mini-batch approaches, such as node-wise, layer-wise, and subgraph sampling, have been proposed to allevia...
Computer Graphics Neural Networks, Computer Algorithms Graph Neural Networks
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Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

Interdisciplinary sciences, computational life sciences Nov 14, 2024
The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural ...
Protein Conformation Graph Neural Networks Proteins Ligands Algorithms Protein Binding Neural Networks, Computer
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Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Oct 9, 2024
Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversi...
Gene Expression Profiling Prefrontal Cortex Epithelial-Mesenchymal Transition Cell Communication Mice Humans Breast Neoplasms Animals Datasets as Topic Graph Neural Networks
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Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features.

International journal of computer assisted radiology and surgery Oct 7, 2024
PURPOSE: This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed s...
Image Interpretation, Computer-Assisted Graph Neural Networks Neural Networks, Computer Radiomics Skin Diseases Humans
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IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network.

Journal of advanced research Aug 7, 2024
INTRODUCTION: Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI trea...
Humans Neoplasms Deep Learning Immune Checkpoint Inhibitors Neural Networks, Computer Tumor Microenvironment Graph Neural Networks Immunotherapy Treatment Outcome
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A dual path graph neural network framework for dementia diagnosis.

Scientific reports Jul 2, 2025
Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have been widely employed to model complex brain networks. However, leveraging the complementary temporal,...
Humans Neural Networks, Computer Magnetic Resonance Imaging Nerve Net Graph Neural Networks Brain Dementia Algorithms
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Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations.

Toxicological sciences : an official journal of the Society of Toxicology Jul 1, 2025
Transcriptomic analyses have been an effective approach to investigate the biological responses and metabolic perturbations by environmental contaminants in rodent models. However, it is well recognized that metabolic networks are highly connected an...
Gene Expression Profiling Graph Neural Networks Transcriptome Liver Environmental Pollutants Metabolic Networks and Pathways Polychlorinated Dibenzodioxins Mice Neural Networks, Computer Animals
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