Výsledky vyhledávání - Graph convolutional autoencoder with attention

Semantic meta-path enhanced global and local topology learning for lncRNA-disease association prediction Autor Xuan, Ping, Zhao, Yue, Cui, Hui, Zhan, Linyun, Jin, Qiangguo, Zhang, Tiangang, Nakaguchi, Toshiya

“… We propose a new prediction method, MGLDA, to encode and integrate the semantics of multiple meta-paths, the global topology of heterogeneous graph, and pairwise attributes of lncRNA and disease nodes…”
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Graph Attention Convolutional Autoencoder-Based Unsupervised Nonlinear Unmixing for Hyperspectral Images Autor Jin, Danni, Yang, Bin

“… This paper proposes a graph attention convolutional autoencoder architecture for hyperspectral unmixing…”
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ERA-WGAT: Edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose CT denoising Autor Liu, Han, Liao, Peixi, Chen, Hu, Zhang, Yi

“… and a window-based graph attention convolutional network that combines static and dynamic attention modules to explore non-local self-similarity…”
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GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction Autor Xuan, Ping, Fan, Mengsi, Cui, Hui, Zhang, Tiangang, Nakaguchi, Toshiya

“… First, a framework based on graph convolutional autoencoder is constructed to learn attention-enhanced topological embedding that integrates the topology structure…”
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MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder Autor Wang, Ying, Gao, Ying-Lian, Wang, Juan, Li, Feng, Liu, Jin-Xing

“… Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA…”
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Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations Autor Xuan, Ping, Gao, Ling, Sheng, Nan, Zhang, Tiangang, Nakaguchi, Toshiya

“…Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods…”
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Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes Autor Xuan, Ping, Cui, Hui, Zhang, Hongda, Zhang, Tiangang, Wang, Linlin, Nakaguchi, Toshiya, Duh, Henry B.L.

“… We propose a novel dynamic graph convolution (DGC) autoencoder with node-attribute-wise attention (NodeAttri-Attention…”
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Graph convolutional network based on self-attention variational autoencoder and capsule contrastive learning for aspect-based sentiment analysis Autor Wang, Xinyue, Liu, Long, Chen, Zhuo, Wang, Haiyan, Yu, Bin

“… In response to these issues, this article puts forward a hybrid graph convolutional network (GCN…”
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Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations Autor Ping Xuan, Ling Gao, Nan Sheng, Tiangang Zhang, Toshiya Nakaguchi

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Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes Autor Ping Xuan, Hui Cui, Hongda Zhang, Tiangang Zhang, Linlin Wang, Toshiya Nakaguchi, Henry B.L. Duh

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GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction Autor Hui Cui, Toshiya Nakaguchi, Ping Xuan, Mengsi Fan, Tiangang Zhang

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scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data Autor Zhang, Lin, Xiang, Haiping, Wang, Feng, Chen, Zepeng, Shen, Mo, Ma, Jiani, Liu, Hui, Zheng, Hongdang

“…•Integrating autoencoder and graph attention autoencoder to effectively extract cell expression and structural information…”
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A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering Autor Tang, Binhua, Feng, Yingying, Gao, Xinyu

“…) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis…”
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Batch Process Monitoring with Attention-Based Two-Dimensional Stacked Graph Convolutional Autoencoder Autor Gao, Xingke, Zhang, Zheng, Gao, Furong, Zhu, Jinlin

“…In batch processes, the efficacy of statistical control is reportedly sensitive to the dynamics and nonlinearity found in the batch data, which can hamper the…”
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Predicting potential microbe-disease associations based on heterogeneous graph attention network and deep sparse autoencoder Autor Wang, Bo, Zhao, Wenlong, Du, Xiaoxin, Zhang, Jianfei, Zhang, Chunyu, Wang, Liping, He, Yang

“… We propose a computational framework called graph attention convolutional deep sparse autoencoder microbe-disease association (GCDSAEMDA…”
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Graph-informed convolutional autoencoder to classify brain responses during sleep Autor Zakeri, Sahar, Makouei, Somayeh, Danishvar, Sebelan

“…Automated machine-learning algorithms that analyze biomedical signals have been used to identify sleep patterns and health issues. However, their performance…”
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A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder Autor Ni, Yanchun, Jin, Qiyuan, Hu, Rui

“… This paper proposes an autoencoder model integrating Temporal Convolutional Networks (TCN) and Graph Attention Networks (GAT…”
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AEGCN: An Autoencoder-Constrained Graph Convolutional Network Autor Ma, Mingyuan, Na, Sen, Wang, Hongyu

“…We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains…”
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Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder Autor Leng, Jiacheng, Yu, Jiating, Wu, Ling-Yun, Chen, Hongyang

“… To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining…”
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DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition Autor Zhu, Yida, Luo, Haiyong, Chen, Runze, Zhao, Fang

“… We further introduce an attention-based graph convolutional network to construct new heterogeneous…”
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