AIMC Topic: Graph Neural Networks

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GCPNet: An interpretable Generic Crystal Pattern graph neural Network for predicting material properties.

Neural networks : the official journal of the International Neural Network Society Apr 12, 2025
To predict material properties from crystal structures, we introduce a simple yet flexible Generic Crystal Pattern graph neural Network (GCPNet), which is based on crystal pattern graphs and employs the Graph Convolutional Attention Operator (GCAO) a...
Graph Neural Networks Crystallization Calcium Compounds Neural Networks, Computer Oxides Titanium Algorithms
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Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.

Medical image analysis Apr 9, 2025
Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitate...
Humans Neural Networks, Computer Brain Graph Neural Networks Connectome Diffusion Tensor Imaging Magnetic Resonance Imaging Multimodal Imaging
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Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.

BMC cancer Apr 9, 2025
OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise i...
Graph Neural Networks Immunotherapy Gene Expression Regulation, Neoplastic Tumor Microenvironment Prognosis Transcriptome Gene Expression Profiling Biomarkers, Tumor Melanoma Computational Biology Humans Skin Neoplasms Neural Networks, Computer
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HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions.

Journal of chemical information and modeling Apr 4, 2025
Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versatility as a solvent. However, accurately predicting...
Hydrogen-Ion Concentration Hydrolysis Graph Neural Networks Thermodynamics Neural Networks, Computer
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Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification.

IEEE journal of biomedical and health informatics Apr 4, 2025
Fundus disease is a complex and universal disease involving a variety of pathologies. Its early diagnosis using fundus images can effectively prevent further diseases and provide targeted treatment plans for patients. Recent deep learning models for ...
Fundus Oculi Image Interpretation, Computer-Assisted Neural Networks, Computer Deep Learning Retinal Diseases Graph Neural Networks Humans
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Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks.

Journal of chemical information and modeling Mar 31, 2025
Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture ric...
Graph Neural Networks Neural Networks, Computer Drug Discovery Proteins Pharmaceutical Preparations
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DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.

Journal of chemical information and modeling Mar 27, 2025
Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance and have had profound impact in applications that include drug discove...
Graph Neural Networks Neural Networks, Computer Molecular Dynamics Simulation Software Quantum Theory
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Temporal and spatial feature extraction using graph neural networks for multi-point water quality prediction in river network areas.

Water research Mar 26, 2025
Deep learning methods have demonstrated strong capabilities in capturing nonlinear relationships for water quality prediction, yet existing studies predominantly focus on individual monitoring sites while neglecting pollutant spatial dynamics. To add...
Neural Networks, Computer Environmental Monitoring Oxygen Water Quality Rivers Nitrogen Graph Neural Networks
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GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.

Pharmaceutical research Mar 26, 2025
PURPOSE: The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play a significant role during action potential repolarization of cardiomyocytes. Blockade of the hERG channel by off-target drugs can lead to long QT syn...
ERG1 Potassium Channel Graph Neural Networks Ether-A-Go-Go Potassium Channels Potassium Channel Blockers Neural Networks, Computer Deep Learning Machine Learning Algorithms Humans Action Potentials Computer Simulation Cardiotoxicity
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A graph neural network explainability strategy driven by key subgraph connectivity.

Journal of biomedical informatics Mar 21, 2025
Current explainability strategies for Graph Neural Networks (GNNs) often focus on individual nodes or edges, neglecting the significance of key subgraphs in decision-making processes. This limitation can result in dispersed and less reliable explanat...
Humans Neural Networks, Computer Computer Graphics Graph Neural Networks Algorithms
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