Search Results - "variational graph autoencoder"

Importance weighted variational graph autoencoder

Authors: Yuhao Tao Lin Guo Shuchang Zhao et al.

Source: Complex & Intelligent Systems, Vol 12, Iss 1, Pp 1-16 (2025)

Subject Terms: Graph neural networks, Variational graph autoencoder, Variational inference, Importance weighted, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053

Access URL: https://doaj.org/article/b111fb84353f458ab68cb00bba7ce0ad

HPOseq: a deep ensemble model for predicting the protein-phenotype relationships based on protein sequences

Authors: Kai Zhao Zhuocheng Ji Linlin Zhang et al.

Source: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-15 (2025)

Subject Terms: Ensemble model, Variational graph autoencoder, Deep learning, Amino acid sequence, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

File Description: electronic resource

Relation: https://doaj.org/toc/1471-2105

Access URL: https://doaj.org/article/c576aff5241d4fa0b88361542a81d0ad

Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines

Authors: Zhina Wang Yangyuan Chen Hongming Ma et al.

Source: Front Pharmacol
Frontiers in Pharmacology, Vol 15 (2025)

Subject Terms: Pharmacology, lung cancer, redevelopment of traditional medicines, variational graph autoencoder (VGAE), snoRNA therapeutic targets, Therapeutics. Pharmacology, RM1-950, artificial intelligence (AI)

Access URL: https://doaj.org/article/c553efc486264dfca34df3bbee44c6bc

Prediction of Associations between Nanoparticle, Drug and Cancer Using Variational Graph Autoencoder

Authors: Emrah İNAN

Source: Volume: 26, Issue: 76 167-172
Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi

Subject Terms: Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer), Varyasyonel Çizge Otokodlayıcı, Nanoparçacıklar, İlaç-hastalık İlişkisi, Variational Graph Autoencoder, Nanoparticles, Drug–disease Association, Computer Vision and Multimedia Computation (Other), 3. Good health

File Description: application/pdf

Access URL: https://dergipark.org.tr/tr/pub/deumffmd/issue/82757/1300214

A one-step graph clustering method on heterogeneous graphs via variational graph embedding

Authors: Chuang Ma Helong Xia

Source: Electronic Research Archive, Vol 32, Iss 4, Pp 2772-2788 (2024)

Subject Terms: T57-57.97, Applied mathematics. Quantitative methods, heterogeneous graph, graph cluster, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, graph neural network, 02 engineering and technology, variational graph autoencoder, Mathematics

Access URL: https://doaj.org/article/8d75186f49114cfc9fc0e5201f30ccc3

Joint Spectral–Spatial Representation Learning for Unsupervised Hyperspectral Image Clustering

Authors: Xuanhao Liu Taimao Wang Xiaofeng Wang

Source: Applied Sciences, Vol 15, Iss 16, p 8935 (2025)

Subject Terms: hyperspectral image clustering, graph attention network, variational graph autoencoder, quantum particle swarm optimization, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/16/8935; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/39fc07e9b34247dfbb478899b58a9ef9

Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation

Authors: Huan Zhu Hongxia Hao Liang Yu

Source: BMC Biology, Vol 22, Iss 1, Pp 1-15 (2024)

Subject Terms: Variational graph autoencoder, Microbe-disease association, Signed message propagation, XGBoost, Biology (General), QH301-705.5

File Description: electronic resource

Relation: https://doaj.org/toc/1741-7007

Access URL: https://doaj.org/article/dac9fc4571f045d086486c10341093e2

Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks

Authors: Zhenguo Xu Jun Li Irene Moulitsas et al.

Source: Big Data and Cognitive Computing ; Volume 9 ; Issue 4 ; Pages: 101

Subject Terms: key node identification, community detection, link prediction, small-world network, graph attention networks, variational graph autoencoder, network efficiency and robustness

File Description: application/pdf

Relation: https://dx.doi.org/10.3390/bdcc9040101

Availability: https://doi.org/10.3390/bdcc9040101

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

Authors: Huan Zhu Hongxia Hao Liang Yu

Source: BMC Biology, Vol 21, Iss 1, Pp 1-15 (2023)

Subject Terms: Variational graph autoencoder, Wasserstein distance, Microbe-disease association, XGBoost, Biology (General), QH301-705.5

File Description: electronic resource

Relation: https://doaj.org/toc/1741-7007

Access URL: https://doaj.org/article/9df9f57d7db64eb1807009ed91888afe

Semantic-Enhanced Variational Graph Autoencoder for Movie Recommendation: An Innovative Approach Integrating Plot Summary Information and Contrastive Learning Strategy

Authors: Wang, Mingye Hu, Xiaohui Xie, Pan et al.

Source: Information Technology and Control ; Vol. 53 No. 2 (2024); 570-583 ; 2335-884X ; 1392-124X

Subject Terms: Variational graph autoencoder, recommender system, graph neural network, semantic vectors, deep learning, contrastive learning

File Description: application/pdf

Relation: https://itc.ktu.lt/index.php/ITC/article/view/35689/16456; https://itc.ktu.lt/index.php/ITC/article/view/35689

Availability: https://itc.ktu.lt/index.php/ITC/article/view/35689

Link Activation Using Variational Graph Autoencoders

Authors: Saeed Jamshidiha Vahid Pourahmadi Abbas Mohammadi et al.

Source: IEEE Communications Letters. 25:2358-2361

Subject Terms: Bayesian deep learning, wireless networks, Graph embedding, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, variational graph autoencoder

File Description: application/pdf

Access URL: http://jultika.oulu.fi/files/nbnfi-fe2021101450946.pdf
http://urn.fi/urn:nbn:fi-fe2021101450946

scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data

Authors: Lijun Liu Xiaoyang Wu Jun Yu et al.

Source: Biology, Vol 13, Iss 9, p 713 (2024)

Subject Terms: scRNA-seq, unsupervised clustering, variational graph autoencoder, graph attention networks, Biology (General), QH301-705.5

File Description: electronic resource

Relation: https://www.mdpi.com/2079-7737/13/9/713; https://doaj.org/toc/2079-7737

Access URL: https://doaj.org/article/6e18ca828f904b919c419f513f2be47b

Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation

Authors: Liang, Guojun Abiri, Najmeh Hashemi, Atiye Sadat et al.

Contributors: Liang, Guojun Abiri, Najmeh Hashemi, Atiye Sadat et al.

Subject Terms: Probability Theory and Statistics, Diffusion model, imputation, multivariate time series, variational graph autoencoder

Relation: http://dx.doi.org/10.1109/ICASSP49660.2025.10888912; scopus:105003891405

Availability: https://lup.lub.lu.se/record/ba804bc3-0bb9-4239-b6d2-54f9ef1e9c7f
https://doi.org/10.1109/ICASSP49660.2025.10888912

De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc

Authors: Runze Li Xuerui Yang

Source: Genome Biology, Vol 23, Iss 1, Pp 1-24 (2022)

Subject Terms: Single-cell spatial transcriptome, Cell interaction, Variational graph autoencoder, VGAE, Deep learning, Biology (General), QH301-705.5, Genetics, QH426-470

File Description: electronic resource

Relation: https://doaj.org/toc/1474-760X

Access URL: https://doaj.org/article/2e8dd2b4f9d942369b5340796ece6ba8

Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors

Authors: Tien Huu Do Evaggelia Tsiligianni Xuening Qin et al.

Contributors: Tien Huu Do Evaggelia Tsiligianni Xuening Qin et al.

Source: IEEE Internet of Things Journal. 7:8943-8955

Subject Terms: IoT, variational graph autoencoder (VGAE), Smart Cities, 13. Climate action, nternet of Things (IoT), 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Deep learning, 02 engineering and technology, 7. Clean energy

Access URL: https://dblp.uni-trier.de/db/journals/iotj/iotj7.html#DoTQHMPD20
https://ieeexplore.ieee.org/abstract/document/9106368
https://biblio.ugent.be/publication/8676438
https://researchportal.vub.be/nl/publications/graph-deep-learning-based-inference-of-fine-grained-air-quality-f
https://biblio.vub.ac.be/vubir/graphdeeplearningbased-inference-of-finegrained-air-quality-from-mobile-iot-sensors(c1df178b-dd01-4bce-ab81-ec9243b2b964).html

Adversarial Attention-Based Variational Graph Autoencoder

Authors: Ziqiang Weng Weiyu Zhang Wei Dou

Source: IEEE Access, Vol 8, Pp 152637-152645 (2020)

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, adversarial mechanism, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Attention layers, variational graph autoencoder, TK1-9971

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8948470/09171337.pdf
https://doaj.org/article/36c14220733f4a9a88d0654312455cd5
https://ieeexplore.ieee.org/document/9171337
https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002232113431020
https://dblp.uni-trier.de/db/journals/access/access8.html#WengZD20

scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data

Authors: Eric Lin Boyuan Liu Leann Lac et al.

Source: Machine Learning: Science and Technology, Vol 4, Iss 3, p 035013 (2023)

Subject Terms: Gaussian mixture model, variational graph autoencoder, single cell RNA-seq, scGMM-VGAE, cell type, model integration, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

Relation: https://doi.org/10.1088/2632-2153/acd7c3; https://doaj.org/toc/2632-2153; https://doaj.org/article/2ad3fa2850704c3a942a46b003492d03

Availability: https://doi.org/10.1088/2632-2153/acd7c3
https://doaj.org/article/2ad3fa2850704c3a942a46b003492d03

scVGAMF: a novel imputation method for scRNA-seq data by integrating linear and non-linear features.

Authors: Zhou, Zhiyuan Zhang, Wei Zheng, Xiaoying et al.

Source: Brief Bioinform ; ISSN:1477-4054 ; Volume:26 ; Issue:5

Subject Terms: imputation, non-negative matrix factorization, scRNA-seq data, variational graph autoencoder

Relation: https://doi.org/10.1093/bib/bbaf562; https://pubmed.ncbi.nlm.nih.gov/41139927; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12554638/

Availability: https://doi.org/10.1093/bib/bbaf562
https://pubmed.ncbi.nlm.nih.gov/41139927
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12554638/

Link activation using variational graph autoencoders

Authors: Jamshidiha, S. (Saeed) Pourahmadi, V. (Vahid) Mohammadi, A. (Abbas) et al.

Subject Terms: Bayesian deep learning, Graph embedding, variational graph autoencoder, wireless networks

File Description: application/pdf

Availability: http://urn.fi/urn:nbn:fi-fe2021101450946

Interpretable Variational Graph Autoencoder with Noninformative Prior

Authors: Lili Sun Xueyan Liu Min Zhao et al.

Source: Future Internet, Vol 13, Iss 2, p 51 (2021)

Subject Terms: neural networks, network representation learning, noninformative prior distribution, variational graph autoencoder, deep learning, Information technology, T58.5-58.64

Relation: https://www.mdpi.com/1999-5903/13/2/51; https://doaj.org/toc/1999-5903; https://doaj.org/article/cc2cb11b99f14c30a56a11f8911d2156

Availability: https://doi.org/10.3390/fi13020051
https://doaj.org/article/cc2cb11b99f14c30a56a11f8911d2156