AIMC Topic: Graph Neural Networks

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A small-scale data driven and graph neural network based toxicity prediction method of compounds.

Computational biology and chemistry Feb 25, 2025
Toxicity prediction is crucial in drug discovery, helping identify safe compounds and reduce development risks. However, the lack of known toxicity data for most compounds is a major challenge. Recently, data-driven models have gained attention as a ...
Drug-Related Side Effects and Adverse Reactions Machine Learning Graph Neural Networks Drug Discovery Humans Neural Networks, Computer
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Predicting social anxiety disorder based on communication logs and social network data from a massively multiplayer online game: Using a graph neural network.

Psychiatry and clinical neurosciences Feb 25, 2025
AIM: Social anxiety disorder (SAD) is a mental disorder that requires early detection and treatment. However, some individuals with SAD avoid face-to-face evaluations, which leads to delayed detection. We aim to predict individuals with SAD based on ...
Graph Neural Networks Video Games Social Networking Japan Phobia, Social Humans Communication Neural Networks, Computer Young Adult Male Female Middle Aged Adult
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Label as Equilibrium: A performance booster for Graph Neural Networks on node classification.

Neural networks : the official journal of the International Neural Network Society Feb 22, 2025
Graph Neural Network (GNN) is effective in graph mining and has become a dominant solution to the node classification task. Recently, a series of label reuse approaches emerged to boost the node classification performance of GNN. They repeatedly inpu...
Data Mining Graph Neural Networks Neural Networks, Computer Algorithms Humans
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Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study.

Journal of affective disorders Feb 21, 2025
BACKGROUND: There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but also explore whether these features have neurobiological correlates.
Male Brain Depression Female Connectome Humans Adolescent Young Adult Nerve Net Magnetic Resonance Imaging Adult Graph Neural Networks Positron-Emission Tomography
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Adaptable graph neural networks design to support generalizability for clinical event prediction.

Journal of biomedical informatics Feb 15, 2025
OBJECTIVE: While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges w...
Humans Machine Learning Algorithms Patient Discharge Neural Networks, Computer Graph Neural Networks Electronic Health Records Deep Learning
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Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural Network.

Environmental science & technology Feb 12, 2025
Deciphering and mitigating dynamic greenhouse gas (GHG) emissions under environmental fluctuation in urban drainage systems (UDGSs) is challenging due to the absence of a high-prediction model that accurately quantifies the contributions of biologica...
Greenhouse Gases Nitrous Oxide Graph Neural Networks Wastewater Methane Neural Networks, Computer
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Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks.

Medical image analysis Feb 7, 2025
The striatum, a crucial part of the basal ganglia, plays a key role in various brain functions through its interactions with the cortex. The complex structural and functional diversity across subdivisions within the striatum highlights the necessity ...
Graph Neural Networks Neural Networks, Computer Algorithms Corpus Striatum Diffusion Tensor Imaging Image Processing, Computer-Assisted Humans
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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...
Graph Neural Networks Datasets as Topic Autoencoder Generative Artificial Intelligence Supervised Machine Learning
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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...
Thermodynamics Graph Neural Networks Neural Networks, Computer Proteins Machine Learning Molecular Dynamics Simulation
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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...
Neural Networks, Computer Machine Learning Proteins Ligands Graph Neural Networks Drug Discovery Protein Binding
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