AIMC Topic: Graph Neural Networks

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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...
Deep Learning Kinetics Neural Networks, Computer Enzymes Graph Neural Networks
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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...
Databases, Genetic Genetic Variation Computational Biology Graph Neural Networks Neural Networks, Computer Humans Deep Learning
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Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.

Briefings in bioinformatics Mar 4, 2025
Synthetic lethality (SL) is a promising gene interaction for cancer therapy. Recent SL prediction methods integrate knowledge graphs (KGs) into graph neural networks (GNNs) and employ attention mechanisms to extract local subgraphs as explanations fo...
Computational Biology Synthetic Lethal Mutations Graph Neural Networks Algorithms Neoplasms Humans Neural Networks, Computer
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Graph neural networks for single-cell omics data: a review of approaches and applications.

Briefings in bioinformatics Mar 4, 2025
Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data ha...
Proteomics Graph Neural Networks Computational Biology Epigenomics Humans Neural Networks, Computer Single-Cell Analysis Genomics Deep Learning
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Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.

Briefings in bioinformatics Mar 4, 2025
High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primarily focus on feature extraction or employ simplistic fully connected layers within prognostic modules,...
Humans Prognosis Biomarkers, Tumor Neoplasms Neural Networks, Computer Algorithms Gene Expression Regulation, Neoplastic Gene Expression Profiling Proportional Hazards Models Computational Biology Graph Neural Networks
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A comprehensive graph neural network method for predicting triplet motifs in disease-drug-gene interactions.

Bioinformatics (Oxford, England) Feb 4, 2025
MOTIVATION: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph moti...
Disease Graph Neural Networks Neural Networks, Computer Algorithms Humans Computational Biology
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T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks.

PloS one Jan 1, 2025
Recently, accurate traffic flow prediction has become a significant part of intelligent transportation systems, which can not only satisfy citizens' travel need and life satisfaction, but also benefit urban traffic management and control. However, tr...
Forecasting Transportation Graph Neural Networks Neural Networks, Computer Algorithms Humans
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iProtDNA-SMOTE: Enhancing protein-DNA binding sites prediction through imbalanced graph neural networks.

PloS one Jan 1, 2025
Protein-DNA interactions play a crucial role in cellular biology, essential for maintaining life processes and regulating cellular functions. We propose a method called iProtDNA-SMOTE, which utilizes non-equilibrium graph neural networks along with p...
DNA-Binding Proteins Binding Sites Graph Neural Networks Databases, Protein DNA Neural Networks, Computer Algorithms Protein Binding Software Computational Biology
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mGNN-bw: Multi-Scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2025
In recent years, computationally assisted diagnosis for classifying autism spectrum disorder (ASD) and typically developing (TD) individuals based on neuroimaging data, such as functional magnetic resonance imaging (fMRI), has garnered significant at...
Graph Neural Networks Autism Spectrum Disorder Brain Humans Female Magnetic Resonance Imaging Male Neural Networks, Computer Neuroimaging Nerve Net Child Algorithms
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GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes.

IEEE journal of biomedical and health informatics Dec 1, 2024
Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming increasingly important for reducing time and cost. Most interaction-based DTI models rely on the guilt-...
Drug Repositioning Algorithms Drug Discovery Pharmaceutical Preparations Graph Neural Networks Humans Computational Biology Drug Interactions Neural Networks, Computer
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