AIMC Topic: Graph Neural Networks

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Generative and contrastive graph representation learning with message passing.

Neural networks : the official journal of the International Neural Network Society Feb 6, 2025
Self-supervised graph representation learning (SSGRL) has emerged as a promising approach for graph embeddings because it does not rely on manual labels. SSGRL methods are generally divided into generative and contrastive approaches. Generative metho...
Autoencoder Graph Neural Networks Generative Artificial Intelligence Supervised Machine Learning Datasets as Topic
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Scaling Graph Neural Networks to Large Proteins.

Journal of chemical theory and computation Feb 6, 2025
Graph neural network (GNN) architectures have emerged as promising force field models, exhibiting high accuracy in predicting complex energies and forces based on atomic identities and Cartesian coordinates. To expand the applicability of GNNs, and m...
Machine Learning Proteins Thermodynamics Neural Networks, Computer Molecular Dynamics Simulation Graph Neural Networks
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CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Feb 6, 2025
In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introduces a novel self-supervised lear...
Machine Learning Drug Discovery Neural Networks, Computer Ligands Protein Binding Graph Neural Networks Proteins
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Breast cancer image classification based on H&E staining using a causal attention graph neural network model.

Medical & biological engineering & computing Feb 4, 2025
Breast cancer image classification remains a challenging task due to the high-resolution nature of pathological images and their complex feature distributions. Graph neural networks (GNNs) offer promising capabilities to capture local structural info...
Neural Networks, Computer Algorithms Female Humans Image Processing, Computer-Assisted Breast Neoplasms Image Interpretation, Computer-Assisted ROC Curve Graph Neural Networks
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Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Scientific reports Feb 3, 2025
Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the...
Graph Neural Networks Convolutional Neural Networks Models, Neurological Schizophrenia Nerve Net Electroencephalography Female Adult Male Humans Algorithms
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AFMDD: Analyzing Functional Connectivity Feature of Major Depressive Disorder by Graph Neural Network-Based Model.

Journal of computational biology : a journal of computational molecular cell biology Feb 3, 2025
The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent years, with the development of deep learning, several methods have been proposed to assist in the di...
Neural Networks, Computer Algorithms Biomarkers Humans Deep Learning Graph Neural Networks Brain Depressive Disorder, Major
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A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features.

BMC public health Feb 1, 2025
BACKGROUND: Accurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph neural network (GNN)-based method to address the issue of cross-regional connectivity in predicting in...
Humans Graph Neural Networks Disease Outbreaks Neural Networks, Computer Spatio-Temporal Analysis Forecasting Influenza, Human
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ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions.

Methods (San Diego, Calif.) Jan 30, 2025
RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biological processes through specific RNA molecule interactions. Previous studies have revealed the indisp...
Humans Neural Networks, Computer Protein Binding Computational Biology RNA Machine Learning RNA-Binding Proteins Graph Neural Networks Algorithms
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Graph Neural Network Learning on the Pediatric Structural Connectome.

Tomography (Ann Arbor, Mich.) Jan 29, 2025
PURPOSE: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), s...
Graph Neural Networks Female Machine Learning Magnetic Resonance Imaging Adolescent Humans Male Adult Connectome Brain Child Neural Networks, Computer
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Adaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.

Interdisciplinary sciences, computational life sciences Jan 28, 2025
 Combination therapy, which synergistically enhances treatment efficacy and inhibits disease progression through the combined effects of multiple drugs, has emerged as a mainstream approach for treating complex diseases and alleviating symptoms. Howe...
Graph Neural Networks Humans Algorithms Drug Interactions Neural Networks, Computer
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