AIMC Topic: Graph Neural Networks

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Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors.

Journal of chemical information and modeling May 26, 2025
The fusion of traditional chemical descriptors with graph neural networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from this integrat...
Ligands Graph Neural Networks Drug Discovery Drug Evaluation, Preclinical Neural Networks, Computer
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Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images.

Scientific reports May 25, 2025
In computational pathology, extracting and representing spatial features from gigapixel whole slide images (WSIs) are fundamental tasks, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of analyzing WSIs...
Image Processing, Computer-Assisted Lung Neoplasms Graph Neural Networks Adenocarcinoma of Lung Algorithms Prognosis Humans Neural Networks, Computer
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Graph Neural Networks for Gleason Grading in Prostate Histopathology Images.

Studies in health technology and informatics May 15, 2025
Prostate cancer is a leading cause of cancer-related deaths, with Gleason grading being key for assessing tumor aggressiveness. We propose a Graph Neural Network-based approach to automate Gleason grading using the Automated Gleason Grading Challenge...
Humans Neural Networks, Computer Neoplasm Grading Male Image Interpretation, Computer-Assisted Prostatic Neoplasms Graph Neural Networks
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M-GNN: A Graph Neural Network Framework for Lung Cancer Detection Using Metabolomics and Heterogeneous Graph Modeling.

International journal of molecular sciences May 13, 2025
Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection critical for improving survival rates, yet conventional methods like CT scans often yield high false-positive rates. This study introduces M-GNN, a grap...
Humans Aged Male Middle Aged Neural Networks, Computer Female Graph Neural Networks Early Detection of Cancer Metabolomics ROC Curve Metabolome Lung Neoplasms
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Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning.

Journal of chemical theory and computation May 13, 2025
Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to...
Proteins Protein Conformation Graph Neural Networks Neural Networks, Computer Machine Learning
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Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks.

NPJ systems biology and applications May 8, 2025
The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a...
Humans Neural Networks, Computer Computational Biology Multiomics Algorithms Transcriptome Alzheimer Disease Gene Expression Profiling Proteomics Genomics Biomarkers Graph Neural Networks
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A Multi-Modal Graph Neural Network Framework for Parkinson's Disease Therapeutic Discovery.

International journal of molecular sciences May 7, 2025
Parkinson's disease (PD) is a complex neurodegenerative disorder lacking effective disease-modifying treatments. In this study, we integrated large-scale protein-protein interaction networks with a multi-modal graph neural network (GNN) to identify a...
Graph Neural Networks Parkinson Disease Drug Repositioning Humans Drug Discovery Protein Interaction Maps Neural Networks, Computer
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H2GnnDTI: hierarchical heterogeneous graph neural networks for drug-target interaction prediction.

Bioinformatics (Oxford, England) Mar 29, 2025
MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying DTIs necessitate computational tools for auto...
Pharmaceutical Preparations Graph Neural Networks Computational Biology Neural Networks, Computer Algorithms Proteins Software Drug Discovery Drug Repositioning
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DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks.

Briefings in bioinformatics Mar 4, 2025
The prediction of enzyme kinetic parameters is crucial for screening enzymes with high catalytic efficiency and desired characteristics to catalyze natural or non-natural reactions. Data-driven machine learning models have been explored to reduce exp...
Graph Neural Networks Kinetics Neural Networks, Computer Enzymes Deep Learning
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A graph neural network approach for accurate prediction of pathogenicity in multi-type variants.

Briefings in bioinformatics Mar 4, 2025
Accurate prediction of pathogenic variants in human disease-associated genes would have a profound effect on clinical decision-making; however, it remains a significant challenge due to the overwhelming number of these variants. We propose graph neur...
Graph Neural Networks Databases, Genetic Genetic Variation Deep Learning Neural Networks, Computer Humans Computational Biology
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