AIMC Topic: Graph Neural Networks

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Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.

Interdisciplinary sciences, computational life sciences Sep 26, 2024
Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to...
Humans Neural Networks, Computer Machine Learning Drug Repositioning Large Language Models Language Graph Neural Networks Computational Biology
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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...
Graph Neural Networks Immunotherapy Treatment Outcome Deep Learning Immune Checkpoint Inhibitors Neural Networks, Computer Tumor Microenvironment Humans Neoplasms
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Dual-Path Graph Neural Network with Adaptive Auxiliary Module for Link Prediction.

Big data Mar 25, 2024
Link prediction, which has important applications in many fields, predicts the possibility of the link between two nodes in a graph. Link prediction based on Graph Neural Network (GNN) obtains node representation and graph structure through GNN, whic...
Algorithms Graph Neural Networks Neural Networks, Computer
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PhenoLinker: Phenotype-gene link prediction and explanation using heterogeneous graph neural networks.

Artificial intelligence in medicine Sep 1, 2025
The association of a given human phenotype with a genetic variant remains a critical challenge in biomedical research. We present PhenoLinker, a novel graph-based system capable of associating a score to a phenotype-gene relationship by using heterog...
Algorithms Phenotype Graph Neural Networks Genetic Variation Humans Neural Networks, Computer
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Heterogeneous graph neural networks enhance pressure estimation in water distribution networks.

Water research Sep 1, 2025
Pressure estimation is crucial for efficient operation and management of water distribution networks (WDNs). However, it is often challenged by limited sensor observations. While graph neural networks (GNNs) have been used to improve hydraulic and wa...
Water Water Quality Pressure Neural Networks, Computer Water Supply Graph Neural Networks
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Predicting CircRNA-Disease Associations Based on Heterogeneous Graph Neural Network and Knowledge Graph Attribute Mining Attention.

Interdisciplinary sciences, computational life sciences Sep 1, 2025
The exploration of associations between circular RNAs (circRNAs) and diseases contributes to a deeper understanding of the pathogenesis of diseases. Many computational methods have been proposed for circRNA-disease associations identification. Howeve...
Computational Biology Data Mining RNA, Circular Neural Networks, Computer Algorithms Graph Neural Networks Humans
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A small-scale data driven and graph neural network based toxicity prediction method of compounds.

Computational biology and chemistry Aug 1, 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 Humans Graph Neural Networks Neural Networks, Computer Drug Discovery
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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...
Metabolic Networks and Pathways Environmental Pollutants Graph Neural Networks Polychlorinated Dibenzodioxins Mice Neural Networks, Computer Liver Animals Gene Expression Profiling Transcriptome
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A lightweight graph neural network to predict long-term mortality in coronary artery disease patients: an interpretable causality-aware approach.

Journal of biomedical informatics Jul 1, 2025
BACKGROUND: Coronary artery disease (CAD) causes substantial death toll in the United States and worldwide. While traditional methods for CAD mortality prediction are based on established risk factors, they have significant limitations in accuracy, a...
Humans Machine Learning Female Neural Networks, Computer Logistic Models Coronary Artery Disease Male Risk Factors Graph Neural Networks Middle Aged Aged Risk Assessment
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Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data.

Artificial intelligence in medicine Jul 1, 2025
In healthcare, predictive analysis using unstructured medical data is crucial for gaining insights into patient conditions and outcomes. However, unstructured data, which contains valuable patient information such as symptoms and medical histories, o...
Length of Stay Hospital Mortality Intensive Care Units Graph Neural Networks Neural Networks, Computer Humans Electronic Health Records Machine Learning
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