Suchergebnisse - "Variational graph autoencoder"

Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation von Zhu, Huan, Hao, Hongxia, Yu, Liang

“… Background Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human …”
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Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance von Zhu, Huan, Hao, Hongxia, Yu, Liang

“… Results In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW …”
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VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell–cell communication network von 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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Importance weighted variational graph autoencoder von Tao, Yuhao, Guo, Lin, Zhao, Shuchang, Zhang, Shiqing

“… Variational Graph Autoencoder (VGAE) is a widely explored model for learning the distribution of graph data …”
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Self-Supervised Variational Graph Autoencoder for System-Level Anomaly Detection von Zhang, Le, Cheng, Wei, Xing, Ji, Chen, Xuefeng, Nie, Zelin, Zhang, Shuo, Hong, Junying, Xu, Zhao

“… However, most industrial scenarios are without graphs. Hence, a self-supervised variational graph autoencoders (SS-VGAE) method is proposed …”
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Epitomic Variational Graph Autoencoder von Khan, Rayyan Ahmad, Anwaar, Muhammad Umer, Kleinsteuber, Martin

“… As variational graph autoencoder (VGAE) extends VAE for graph-structured data, it inherits the over-pruning problem …”
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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks von Yang, Yongjian, Li, Guanxun, Zhong, Yan, Xu, Qian, Chen, Bo-Jia, Lin, Yu-Te, Chapkin, Robert S, Cai, James J

“… and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE …”
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Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder von Luo, Xin, Wang, Liwei, Hu, Pengwei, Hu, Lun

“… To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder …”
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Variational graph autoencoder-driven balancing strategy for multimodal multi-objective optimization von Yang, Lei, Zhang, Erlei, Dang, Qianlong

“… Therefore, this paper proposes a multimodal multi-objective evolutionary algorithm driven by variational graph autoencoder (VGAE …”
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MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder von 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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VIGA: A variational graph autoencoder model to infer user interest representations for recommendation von Gan, Mingxin, Zhang, Hang

“… Learning representations of both user interests and item characteristics is essentially important for recommendation tasks. Although graph neural network-based …”
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Dual stream fusion link prediction for sparse graph based on variational graph autoencoder and pairwise learning von Li, Xun, Cai, Hongyun, Feng, Chuan, Zhao, Ao

“… To address these issues, this paper proposes a novel link prediction method for sparse graphs based on variational graph autoencoder and pairwise learning …”
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Camouflaged Variational Graph AutoEncoder Against Attribute Inference Attacks for Cross-Domain Recommendation von Xiong, Yudi, Guo, Yongxin, Pan, Weike, Yang, Qiang, Ming, Zhong, Zhang, Xiaojin, Han, Yu, Lin, Tao, Tang, Xiaoying

“… Cross-domain recommendation (CDR) aims to alleviate the data sparsity problem by leveraging the benefits of modeling two domains. However, existing research …”
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Adversarial Attention-Based Variational Graph Autoencoder von Weng, Ziqiang, Zhang, Weiyu, Dou, Wei

“… graph embedding performance. In this paper, we propose the adversarial attention variational graph autoencoder (AAVGA …”
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PathVGAE: a path-based variational graph autoencoder for learning to rank roads von Wagner, Christopher, Alizadeh, Danial, Dodge, Somayeh

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Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis von Yadalam, Pradeep K., Natarajan, Prabhu Manickam, Ardila, Carlos M.

“… This study evaluates the efficacy of Variational Graph Autoencoders (VGAEs) in reconstructing gene data related to NLRP3-mediated pyroptosis in periodontitis …”
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MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data von Zhao, Jinhui, He, Jiarui, Guan, Pengwei, Bao, Han, Zhao, Xinjie, Zhao, Chunxia, Qin, Wangshu, Lu, Xin, Xu, Guowang

“… To address these critical limitations, we introduce MODAPro, a biologically informed deep learning framework that synergistically integrates variational graph autoencoders (VAE …”
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Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization von Choong, Jun Jin, Liu, Xin, Murata, Tsuyoshi

“… Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs …”
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A Variational Graph Autoencoder Aided Canonical Correlation Analysis-Based Online Abnormal Patterns Detection Method for Buildings HVAC Systems von Pan, Xiaogang, Deng, Qiao, Jiao, Yuanyuan, Chen, Zhiwen

“… Heating, Ventilation, and Air Conditioning (HVAC) systems have become essential components of contemporary life, extensively employed in commercial and …”
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A ^VGAE: An Attribute-Augmented Adversarial Variational Graph Autoencoder for Link Prediction von 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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