Results 11 to 20 of about 18,832 (283)
, 2023 Towards Sufficient Power-Traffic Coordination: GCN-based Prediction of Spatial-Temporal EV Charging ...
浩 成 (15741326)
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GCN sensitive protein translation in yeast [PDF]
PLOS ONE, 2020 Abstract Levels of protein translation by ribosomes are governed both by features of the translation machinery as well as sequence properties of the mRNAs themselves. We focus here on a striking three-nucleotide periodicity, characterized by overrepresentation of GCN codons and underrepresentation of G at the second ...
William A. Barr +11 more
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MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysis
CoRRAlzheimer's Disease (AD) is a currently incurable neurodegeneartive disease. Accurately detecting AD, especially in the early stage, represents a high research priority. AD is characterized by progressive cognitive impairments that are related to alterations in brain functional connectivity (FC).
Wenqi Zhu, Yinghua Fu, Ze Wang
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The translational regulators GCN-1 and ABCF-3 act together to promote apoptosis in C. elegans. [PDF]
PLoS Genetics, 2014 The proper regulation of apoptosis requires precise spatial and temporal control of gene expression. While the transcriptional and translational activation of pro-apoptotic genes is known to be crucial to triggering apoptosis, how different mechanisms ...
Takashi Hirose, H Robert Horvitz
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Hyperspectral Multilevel GCN and CNN Feature Fusion for Change Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral image (HSI) change detection focuses on identifying differences in multitemporal HSIs. Graph convolutional networks (GCNs) have demonstrated greater promise than convolutional neural networks (CNNs) in remote sensing, particularly for ...
Chhaya Katiyar, Vidya Manian
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Mesh evaluation method for shell elements using graph convolutional network
Nihon Kikai Gakkai ronbunshu, 2023 Finite element mesh quality, which affects analysis accuracy and computational cost, is usually determined based on the designer's experience. In this study, we propose a GCN (Graph Convolutional network)-based method for evaluating the quality of ...
Yujin YOKOI +2 more
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Multi-Semantic Alignment Graph Convolutional Network
Connection Science, 2022 Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. However, there are still some challenges for GCN. For example, the model is shallow; the performance is poor when labelled nodes are severely scarce.
Jisheng Qin +3 more
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Connection Science, 2022 Aspect-level sentiment classification, a significant task of fine-grained sentiment analysis, aims to identify the sentimental information expressed in each aspect of a given sentence The existing methods combine global features and local structures to ...
Subo Wei +4 more
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E-GCN: graph convolution with estimated labels [PDF]
, 2021 Graph Convolutional Network (GCN) has been commonly applied for semi-supervised learning tasks. How-ever, the established GCN frequently only considers the given labels in the topology optimization, which may not deliver the best performance for semi ...
Zeng, Xiaoqin +3 more
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Nanomaterials, 2021 A highly mesoporous graphitic carbon nitride g-C3N4 (GCN) has been produced by a template-free method and effectively photodegrade tetracycline (TC) antibiotic under solar light irradiation. The mesoporous GCN (GCN-500) greatly improves the photoactivity
Bao Lee Phoon +4 more
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