Search Results - Graph autoencoder*

Spectral-Spatial Feature Extraction With Dual Graph Autoencoder for Hyperspectral Image Clustering by Zhang, Yongshan, Wang, Yang, Chen, Xiaohong, Jiang, Xinwei, Zhou, Yicong

“… into feature extraction. To address these issues, in this paper, we propose a dual graph autoencoder (DGAE…”
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Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation by Zhu, Huan, Hao, Hongxia, Yu, Liang

“… MSignVGAE employs a graph variational autoencoder to model noisy signed association data and extends the multi-scale concept to enhance representation capabilities…”
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GAM-MDR: probing miRNA–drug resistance using a graph autoencoder based on random path masking by Zhou, Zhecheng, Du, Zhenya, Jiang, Xin, Zhuo, Linlin, Xu, Yixin, Fu, Xiangzheng, Liu, Mingzhe, Zou, Quan

“… To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE…”
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Graph-RPI: predicting RNA–protein interactions via graph autoencoder and self-supervised learning strategies by Guan, Jiahui, Yao, Lantian, Xie, Peilin, Zhao, Zhihao, Meng, Dian, Lee, Tzong-Yi, Wang, Junwen, Chiang, Ying-Chih

“… In this study, we proposed a novel sequence-based RPI prediction framework based on graph neural networks (GNNs…”
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VCGAE++: variational collective graph autoEncoder for multi-behavior recommendation: VCGAE++: variational collective graph autoEncoder for multi-behavior by Zhuang, Yingxuan, Liu, Yang, Pan, Weike, Ming, Zhong

“…Variational autoencoder (VAE) is known as a classic and effective method in modeling users…”
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Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks by Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea, Righi, Marcello

“… Our approach leverages geometric deep learning, specifically through the use of an autoencoder architecture built on graph convolutional networks…”
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VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell–cell communication network by Zhang, Tianjiao, Zhang, Xiang, Wu, Zhenao, Ren, Jixiang, Zhao, Zhongqian, Zhang, Hongfei, Wang, Guohua, Wang, Tao

“…Abstract Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling…”
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SFGAE: a self-feature-based graph autoencoder model for miRNA–disease associations prediction by Ma, Mingyuan, Na, Sen, Zhang, Xiaolu, Chen, Congzhou, Xu, Jin

“… In this study, we resolve this issue by a novel self-feature-based graph autoencoder model, shortened as SFGAE…”
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DPMGCDA: Deciphering circRNA-Drug Sensitivity Associations with Dual Perspective Learning and Path-Masked Graph Autoencoder by Luo, Yue, Deng, Lei

“… To address this challenge, we propose a computational framework termed DPMGCDA leveraging dual perspective learning and path-masked graph autoencoder to predict circRNA-drug sensitivity associations…”
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Masked Graph Autoencoder for Self‐Supervised Transportation Mode Recognition by Zeng, Ziyi, Wang, Guanwen, Zhang, Yifan, Guan, Qingfeng, Yu, Wenhao

“… Notably, in order to learn features without labeled data, we introduce graph autoencoder into the framework…”
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MULGA, a unified multi-view graph autoencoder-based approach for identifying drug–protein interaction and drug repositioning by Ma, Jiani, Li, Chen, Zhang, Yiwen, Wang, Zhikang, Li, Shanshan, Guo, Yuming, Zhang, Lin, Liu, Hui, Gao, Xin, Song, Jiangning

“… information utilization, and reliable negative sample selection, remain to be addressed. Results To address these issues, we propose a novel, unified multi-view graph…”
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MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder by Deng, Zengqian, Xu, Jie, Feng, Yinfei, Dong, Liangcheng, Zhang, Yuanyuan

“…Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees…”
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Dual-decoder graph autoencoder for unsupervised graph representation learning by Sun, Dengdi, Li, Dashuang, Ding, Zhuanlian, Zhang, Xingyi, Tang, Jin

“… Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks…”
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An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing by Alfaro-Mejia, Estefania, Delgado, Carlos J., Manian, Vidya

“… This article introduces the autoencoder graph ensemble model (AEGEM), a novel ensemble-based framework designed to enhance performance in both endmember extraction and abundance estimation…”
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Predicting microbe–disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion by Shi, Kai, Huang, Kai, Li, Lin, Liu, Qiaohui, Zhang, Yi, Zheng, Huilin

“…-disease associations, which is based on graph autoencoder and inductive matrix completion. By co-training the information from microbe and disease space, the new…”
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Early Parkinson's Disease Prediction Using rS-fMRI Functional Connectivity and Autoencoder Graph Convolutional Network by Limas, Lesbia Lopez, Manian, Vidya

“… We propose a deep learning framework that combines resting-state functional MRI (rs-fMRI) data and a Graph Convolutional Network (GCN…”
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Graph-based learning for sleep microarchitecture: a hybrid graph autoencoder and graph attention network approach by Kurisinkal Augustine, Amala Ann, Vaidhehi

“…: We developed a graph autoencoder (GAE) combined with a Graph attention network (GAT) to analyze polysomnography (PSG…”
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Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information by Zhang, Chi, Jung, Jin-Woo

“… Graph autoencoders have been widely utilized for such purposes, leveraging the outstanding capabilities of Graph Neural Networks to model graph structured data…”
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MPI-VGAE: protein–metabolite enzymatic reaction link learning by variational graph autoencoders by Wang, Cheng, Yuan, Chuang, Wang, Yahui, Chen, Ranran, Shi, Yuying, Zhang, Tao, Xue, Fuzhong, Patti, Gary J, Wei, Leyi, Hou, Qingzhen

“…–protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE…”
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A ^VGAE: An Attribute-Augmented Adversarial Variational Graph Autoencoder for Link Prediction by Pan, Zhihong, Zhong, Zhijie, Wei, Lingling, Lin, Yunxuan, Li, Weisheng, Lin, Ronghua, Tang, Yong

“… variational graph autoencoder (A<inline-formula><tex-math notation="LaTeX">{}^{3}</tex-math></inline-formula>VGAE), which can effectively solve the above two problems…”
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