Results 21 to 30 of about 142,397 (310)
PLoS ONE, 2021 Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding ...
Jeongtae Son, Dongsup Kim
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Geometric Deep Learning for Protein–Protein Interaction Predictions
IEEE Access, 2022 This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux +3 more
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Hybrid Graph Models for Traffic Prediction
Applied Sciences, 2023 Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions.
Renyi Chen, Huaxiong Yao
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Graph matching as a graph convolution operator for graph neural networks [PDF]
Pattern Recognition Letters, 2021 Abstract Convolutional neural networks (CNNs), in a few decades, have outperformed the existing state of the art methods in classification context. However, in the way they were formalised, CNNs are bound to operate on euclidean spaces. Indeed, convolution is a signal operation that are defined on euclidean spaces.
Martineau, Maxime +3 more
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Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data
Mathematics, 2023 Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets.
Anna Boronina +2 more
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Pooling in Graph Convolutional Neural Networks [PDF]
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 5 pages, 2 figures, 2019 Asilomar Conference ...
Mark Cheung +4 more
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Multipath Graph Convolutional Neural Networks
CoRR, 2021 Las redes de convolución de gráficos han atraído recientemente mucha atención para el aprendizaje de la representación en espacios de características no euclidianos. Investigaciones recientes se han centrado en el apilamiento de múltiples capas como en las redes neuronales convolucionales para el aumento del poder expresivo de las redes de convolución ...
Rangan Das +3 more
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Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]
Jisuanji kexueNode classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
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Convolutional Graph Neural Networks
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama +3 more
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Transformer-Based Graph Convolutional Network for Sentiment Analysis
Applied Sciences, 2022 Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years.
Barakat AlBadani +4 more
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