AIMC Topic: Graph Neural Networks

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Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.

Computer methods and programs in biomedicine Jun 1, 2025
BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method in clinical practice for cardiovascular diseases. The potential correlation between interlead signals is an important reference for clinical diagnosis ...
ROC Curve Electrocardiography Graph Neural Networks Signal Processing, Computer-Assisted Humans Algorithms Cardiovascular Diseases Neural Networks, Computer Deep Learning
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LGLoc as a new language model-driven graph neural network for mRNA localization.

Scientific reports May 28, 2025
The localization of mRNA is crucial for the synthesis of functional proteins and plays a significant role in cellular processes. Understanding mRNA localization can enhance applications in disease diagnosis (e.g., cancer, Alzheimer's) and drug develo...
Computational Biology RNA, Messenger Graph Neural Networks Machine Learning Algorithms Humans Neural Networks, Computer
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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...
Drug Evaluation, Preclinical Ligands Graph Neural Networks Neural Networks, Computer Drug Discovery
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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...
Graph Neural Networks Lung Neoplasms Adenocarcinoma of Lung Humans Neural Networks, Computer Prognosis Algorithms Image Processing, Computer-Assisted
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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 Neoplasm Grading Image Interpretation, Computer-Assisted Graph Neural Networks Male Prostatic Neoplasms Neural Networks, Computer
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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...
Female Aged Male Humans Neural Networks, Computer Metabolome Graph Neural Networks Lung Neoplasms Early Detection of Cancer Metabolomics Middle Aged ROC Curve
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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...
Graph Neural Networks Proteins Protein Conformation 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...
Algorithms Biomarkers Transcriptome Genomics Gene Expression Profiling Humans Neural Networks, Computer Proteomics Computational Biology Alzheimer Disease Multiomics 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...
Drug Repositioning Graph Neural Networks Protein Interaction Maps Parkinson Disease Neural Networks, Computer Drug Discovery Humans
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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...
Drug Discovery Computational Biology Proteins Graph Neural Networks Software Neural Networks, Computer Drug Repositioning Algorithms Pharmaceutical Preparations
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