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Graph Neural Networks

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Detection of pre-mRNA involved in abnormal splicing using Graph Neural Network and Nearest Correlation Method.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 40040085
BACKGROUND: DNA is the building block of genetic information, and is composed of alternating sequences of exons with genetic information and introns without no genetic information. DNA is damaged by normal metabolic activities and environmental facto...
Algorithms Graph Neural Networks Humans Neural Networks, Computer Polymorphism, Single Nucleotide RNA Precursors RNA Splicing ROC Curve
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Leveraging Graph Neural Networks for MIC Prediction in Antimicrobial Resistance Studies.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 40039779
Antimicrobial resistance (AMR) poses a significant challenge in healthcare and public health, with organisms such as nontyphoidal Salmonella leading the way due to their escalating resistance to antimicrobial agents. This situation severely complicat...
Algorithms Anti-Bacterial Agents Drug Resistance, Bacterial Graph Neural Networks Humans Machine Learning Microbial Sensitivity Tests Neural Networks, Computer
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Novel Alzheimer's Disease Stating Based on Comorbidities-Informed Graph Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 40039558
Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our study employs Graph Neural Networks (GNNs) for multi-cl...
Aged Algorithms Alzheimer Disease Cognitive Dysfunction Comorbidity Female Graph Neural Networks Humans Male Neural Networks, Computer Neuroimaging
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Decoding Visual Perception from EEG Using Explainable Graph Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 40039225
Brain decoding is an emerging area in the fields of neuroscience and machine learning. The goal of decoding is to utilize measured brain activity to understand the thoughts or sensations of individuals. In the fields of computer vision and machine le...
Algorithms Brain Electroencephalography Graph Neural Networks Humans Machine Learning Neural Networks, Computer Signal Processing, Computer-Assisted Visual Perception
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A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 40039048
For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential. Surface electromyography (sEMG) signals obtained from the forearm are commonly employed for this purpose. In this p...
Algorithms Electromyography Forearm Gestures Graph Neural Networks Hand Humans Machine Learning Male Neural Networks, Computer Pattern Recognition, Automated Signal Processing, Computer-Assisted
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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 40039031
Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address th...
Algorithms Genomics Graph Neural Networks Humans Neoplasms Neural Networks, Computer Prognosis
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A small-scale data driven and graph neural network based toxicity prediction method of compounds.

Computational biology and chemistry 40048921
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 Discovery Drug-Related Side Effects and Adverse Reactions Graph Neural Networks Humans Machine Learning Neural Networks, Computer
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FEGGNN: Feature-Enhanced Gated Graph Neural Network for robust few-shot skin disease classification.

Computers in biology and medicine 40056840
Accurate and timely classification of skin diseases is essential for effective dermatological diagnosis. However, the limited availability of annotated images, particularly for rare or novel conditions, poses a significant challenge. Although few-sho...
Algorithms Diagnosis, Computer-Assisted Graph Neural Networks Humans Image Interpretation, Computer-Assisted Neural Networks, Computer Skin Diseases
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Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed.

Scientific reports 40044722
Long short-term memory (LSTM) networks are widely used in biomechanical data analysis but have the significant limitations in interpretability and decision transparency. Combining graph neural networks (GNN) with gate recurrent units (GRU) may offer ...
Athletic Performance Baseball Biomechanical Phenomena Graph Neural Networks Humans Male Neural Networks, Computer
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scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 40042052
Single-cell RNA sequencing (scRNA-seq) has unveiled extensive cellular heterogeneity, yet precise cell type annotation and the identification of novel cell populations remain significant challenges. scHeteroNet, a novel graph neural network framework...
Graph Neural Networks Humans Neural Networks, Computer Sequence Analysis, RNA Single-Cell Analysis
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