AIMC Topic: Graph Neural Networks

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An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, as they represent the physical contacts and functional associations between proteins. Recent advances...
Databases, Protein Protein Interaction Mapping Graph Neural Networks Computational Biology Proteins Neural Networks, Computer Algorithms Protein Interaction Maps Humans
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Super-fast and accurate nonlinear foot deformation Prediction using graph neural networks.

Journal of the mechanical behavior of biomedical materials Dec 9, 2024
Recently, there has been a significant increase in the number of foot diseases, highlighting the importance of non-surgical treatments. Customized insoles, tailored to an individual's foot morphology, have emerged as a promising solution. However, th...
Humans Neural Networks, Computer Finite Element Analysis Time Factors Foot Nonlinear Dynamics Graph Neural Networks Biomechanical Phenomena Mechanical Phenomena
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MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.

Computers in biology and medicine Dec 6, 2024
Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable...
Graph Neural Networks Neoplasms Multiomics Protein Interaction Maps Databases, Genetic Neural Networks, Computer Genomics Computational Biology Gene Regulatory Networks Humans
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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...
Machine Learning Protein Interaction Maps Graph Neural Networks Craniofacial Abnormalities Humans Neural Networks, Computer
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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...
Humans Neural Networks, Computer Brain-Computer Interfaces Imagination Algorithms Time Factors Graph Neural Networks Deep Learning Electroencephalography Signal Processing, Computer-Assisted
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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 Neural Networks, Computer Seasons Spatio-Temporal Analysis Meteorological Concepts Graph Neural Networks Air Pollutants China Air Pollution Cities Environmental Monitoring
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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 Graph Neural Networks Algorithms
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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 ...
Neural Networks, Computer Protein Binding Ligands Protein Conformation Algorithms Proteins Graph Neural Networks
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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 Mice Breast Neoplasms Animals Graph Neural Networks Cell Communication Datasets as Topic Epithelial-Mesenchymal Transition Humans
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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 Radiomics Skin Diseases Humans Neural Networks, Computer
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