Search Results - "Graph autoencoder"

SPICEiST: subcellular RNA pattern enhances cell clustering of imaging-based spatial transcriptomics

Authors: Sungwoo Bae Yuchang Seong Dongjoo Lee et al.

Source: Genomics & Informatics, Vol 23, Iss 1, Pp 1-14 (2025)

Subject Terms: Imaging-based spatial transcriptomics, Gene panel, Graph autoencoder, Subcellular gene expression, Clustering, Tumor microenvironment, Genetics, QH426-470

File Description: electronic resource

Relation: https://doaj.org/toc/2234-0742

Access URL: https://doaj.org/article/a8bfb4ec332a46b19f6d63caff3fb85a

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

Graph Neural Network-Based Approach for Detecting False Data Injection Attacks on Voltage Stability

Authors: Shahriar Rahman Fahim Rachad Atat Cihat Kececi et al.

Source: IEEE Open Access Journal of Power and Energy, Vol 12, Pp 12-23 (2025)

Subject Terms: false data injection attacks, TK1001-1841, Cybersecurity, Production of electric energy or power. Powerplants. Central stations, Distribution or transmission of electric power, voltage stability, voltage regulation, graph autoencoder, TK3001-3521, bad data intrusion

Access URL: https://doaj.org/article/0777ab3aaba449c3a21f51feabf0b528

Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals

Authors: Bardh Prenkaj Mario Villaizán-Vallelado Tobias Leemann et al.

Source: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. :2420-2431

Subject Terms: Redes neuronales gráficas, Explicabilidad contrafactual, Autoencodificadores gráficos, Gráficos dinámicos, counterfactual explainability, dynamic graphs, graph autoencoder, graph neural networks

File Description: application/pdf

CRIBADO VIRTUAL SOBRE REDES BIOLÓGICAS PARA EL DESCUBRIMIENTO DE FÁRMACOS MEDIANTE IA

Authors: Conti, German Gómez Chávez, Leonardo Contardi, Gastón et al.

Subject Terms: Graph Autoencoder, Artificial Intelligence, Melanoma, Protein-Protein Interaction Network

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

Deciphering metabolic disease mechanisms for natural medicine discovery via graph autoencoders

Authors: Qingquan Liao Wei Zhao Zhan Wang et al.

Source: Front Pharmacol
Frontiers in Pharmacology, Vol 16 (2025)

Subject Terms: Pharmacology, graph autoencoder, metabolite-disease associations, Therapeutics. Pharmacology, RM1-950, metabolic diseases, natural medicines, drug discovery

Access URL: https://pubmed.ncbi.nlm.nih.gov/40337507
https://doaj.org/article/486bbb1f80b5439d819efc0e87ed0692

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

Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition

Authors: Hosam Abduljalil Ahmed Elhayek Abdullah Marish Ali et al.

Source: AI, Vol 5, Iss 3, Pp 1695-1708 (2024)

Subject Terms: human activity analysis, Electronic computers. Computer science, graph autoencoder, deep learning, graph convolutional networks, QA75.5-76.95, skeleton-based human action recognition

Access URL: https://doaj.org/article/6db5e7cb49744e039fc486429393c144

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

Graph Anomaly Detection With Disentangled Prototypical Autoencoder for Phishing Scam Detection in Cryptocurrency Transactions

Authors: Junha Kang Seok-Jun Buu

Source: IEEE Access, Vol 12, Pp 91075-91088 (2024)

Subject Terms: representation learning, 0202 electrical engineering, electronic engineering, information engineering, Graph autoencoder, graph neural network, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, anomaly detection, triplet network, TK1-9971

Access URL: https://doaj.org/article/c0be14a0cb0040f68a53bafb7aad9dca

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

Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan

Authors: Hao Guo Yu-Xuan Liu Yao Li et al.

Source: NeuroImage, Vol 310, Iss , Pp 121119- (2025)

Subject Terms: Resting state fMRI, Entire lifespan, Phase coherence graph autoencoder, Metastable state, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1053811925001211; https://doaj.org/toc/1095-9572

Access URL: https://doaj.org/article/3b4f1e561b7542fd9bad91c55cc1d708

WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic

Authors: Siqi Wang Xiaoxiao Zhao Jingyu Qiu et al.

Source: Geo-spatial Information Science, Vol 27, Iss 6, Pp 2023-2047 (2024)

Subject Terms: COVID-19, spatio-temporal artificial intelligence, epidemic prevention and control platform, risk assessment, SIR, graph autoencoder, Mathematical geography. Cartography, GA1-1776, Geodesy, QB275-343

File Description: electronic resource

Relation: https://doaj.org/toc/1009-5020; https://doaj.org/toc/1993-5153

Access URL: https://doaj.org/article/7453ae8fe71c409e964ebdea15a208b7

Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks

Authors: Hanachi, Refka Sellami, Akrem Farah, Imed Riadh et al.

Contributors: Hanachi, Refka Sellami, Akrem Farah, Imed Riadh et al.

Source: Neural Computing and Applications. 36:3737-3759

Subject Terms: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], HSI classification, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Graph autoencoder, Multi-view graph representation learning, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Graph convolutional networks, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

Access URL: https://hal.science/hal-04346576v1
https://doi.org/10.1007/s00521-023-09275-5

INDIVIDUAL TREE SEGMENTATION FROM BLS DATA BASED ON GRAPH AUTOENCODER

Authors: R. Fekry W. Yao A. Sani-Mohammed et al.

Contributors: R. Fekry W. Yao A. Sani-Mohammed et al.

Source: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 547-553 (2023)

Subject Terms: Lidar, Technology, 15. Life on land, Engineering (General). Civil engineering (General), Graph neural network, TA1501-1820, Individual tree segmentation, Backpack laser scanning, Graph autoencoder, Applied optics. Photonics, TA1-2040

File Description: application/pdf

Access URL: https://isprs-annals.copernicus.org/articles/X-1-W1-2023/547/2023/
https://doaj.org/article/b4474b4ee8034f0fb2348b2ef051b53f

Defense Strategy Against False Data Injection Attacks on Cyber–Physical System for Vehicle–Grid Based on KNN-GAE

Authors: Qiuyan Li Dawei Song Yuanyuan Wang et al.

Source: Energies, Vol 18, Iss 19, p 5215 (2025)

Subject Terms: cyber–physical system for vehicle–grid, false data injection attack, k-nearest neighbors, graph autoencoder, state estimation, Technology

File Description: electronic resource

Relation: https://www.mdpi.com/1996-1073/18/19/5215; https://doaj.org/toc/1996-1073

Access URL: https://doaj.org/article/a53345ee0b024d1bb705b78a04ac835f

Multi-Task Graph Attention Net for Electricity Consumption Prediction and Anomaly Detection

Authors: Na Bai Jian Zhang Zhaoli Wu

Source: Computers, Vol 14, Iss 9, p 350 (2025)

Subject Terms: electricity consumption anomaly detection, electricity consumption prediction, multi-task learning, graph autoencoder, sequence entropy, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://www.mdpi.com/2073-431X/14/9/350; https://doaj.org/toc/2073-431X

Access URL: https://doaj.org/article/184b86f77e564534bf7653d6a1220839

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

Drug repositioning based on residual attention network and free multiscale adversarial training

Authors: Guanghui Li Shuwen Li Cheng Liang et al.

Source: BMC Bioinformatics, Vol 25, Iss 1, Pp 1-21 (2024)

Subject Terms: Graph attention network, Residual network, Graph autoencoder, Adversarial training, Drug-disease association, 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/6f22110af321478f8be2cb5e6eae81e8