Search Results - "Graph convolutional autoencoder"

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN

Authors: Ming Zeng Min Wang Fuqiang Xie et al.

Source: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-29 (2025)

Subject Terms: Drug-target interaction, Dynamic weighting convolutional residual connection, Dual self-supervised joint training mechanism, Graph convolutional autoencoder, Generative adversarial network, 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/c9b441325e6d409dbc981ebfca8af0c6

Improving the Adversarial Robustness of Machine Learning-based Phishing Website Detectors: An Autoencoder-based Auxiliary Approach

Authors: Gao, Yang Samtani, Sagar Shah, Ankit

Subject Terms: Cybersecurity in the Age of Artificial Intelligence, AI for Cybersecurity, and Cybersecurity for AI, adversarial robustness, cybersecurity, graph convolutional autoencoder, machine learning, phishing website detection

File Description: application/pdf

Relation: Proceedings of the 58th Hawaii International Conference on System Sciences; https://hdl.handle.net/10125/108886

Availability: https://hdl.handle.net/10125/108886
https://doi.org/10.24251/HICSS.2025.050

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Authors: Peng Chen Haoran Zheng

Source: BMC Bioinformatics, Vol 24, Iss 1, Pp 1-21 (2023)

Subject Terms: Drug-target interaction, Spatial consistency constraint, Graph convolutional autoencoder, Deep learning, 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/79c43c3a72d742d6a41c5871de422089

Temporal graph convolutional autoencoder based fault detection for renewable energy applications.

Authors: Arifeen, Murshedul Petrovski, Andrei

Subject Terms: Temporal graph convolutional autoencoder, Fault detection, Wind turbine, Photovoltaic microgrid

Relation: https://rgu-repository.worktribe.com/output/2446269

Availability: https://doi.org/10.1109/ICPS59941.2024.10639998
https://rgu-repository.worktribe.com/file/2446269/1/ARIFEEN%202024%20Temporal%20graph%20convolutional%20%28AAM%29
https://rgu-repository.worktribe.com/output/2446269

Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects

Authors: Ping Xuan Kai Xu Hui Cui et al.

Source: Front Pharmacol
Frontiers in Pharmacology, Vol 14 (2023)

Subject Terms: representation-level attention, Pharmacology, graph convolutional autoencoder, graph generative and adversarial strategy, topologies and attributes from heterogeneous graphs, Therapeutics. Pharmacology, RM1-950, self-calibrated pairwise attributes, 3. Good health

Access URL: https://pubmed.ncbi.nlm.nih.gov/37731739
https://doaj.org/article/f314529ae8b54209b8807b6de17cc72b

Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects

Authors: P Xuan K Xu Hui Cui et al.

Subject Terms: Pharmacology and pharmaceutical sciences, Neural networks, graph generative and adversarial strategy, topologies and attributes from heterogeneous graphs, graph convolutional autoencoder, self-calibrated pairwise attributes, representation-level attention

Availability: https://doi.org/10.26181/25272016.v1
https://figshare.com/articles/journal_contribution/Graph_generative_and_adversarial_strategy-enhanced_node_feature_learning_and_self-calibrated_pairwise_attribute_encoding_for_prediction_of_drug-related_side_effects/25272016

CVAM: CNA Profile Inference of the Spatial Transcriptome Based on the VGAE and HMM

Authors: Jian Ma Jingjing Guo Zhiwei Fan et al.

Source: Biomolecules, Vol 13, Iss 5, p 767 (2023)

Subject Terms: copy number alteration, HMM, variational graph convolutional autoencoder, spatial transcriptome, Microbiology, QR1-502

Relation: https://www.mdpi.com/2218-273X/13/5/767; https://doaj.org/toc/2218-273X; https://doaj.org/article/90463aa50daa432f92ab1ce3667e38d2

Availability: https://doi.org/10.3390/biom13050767
https://doaj.org/article/90463aa50daa432f92ab1ce3667e38d2

Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series

Authors: Eric Stefan Miele Fabrizio Bonacina Alessandro Corsini

Source: Energy and AI, Vol 8, Iss , Pp 100145- (2022)

Subject Terms: Wind turbine, Condition monitoring, Deep anomaly detection, SCADA data, Graph Convolutional Autoencoder, Multivariate Time series, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666546822000076; https://doaj.org/toc/2666-5468

Access URL: https://doaj.org/article/24c38fb3bba94c47b4048e1cf1c518b2

An adaptive building simplification approach based on shape analysis and representation

Authors: YAN Xiongfeng YUAN Tuo YANG Min et al.

Source: Acta Geodaetica et Cartographica Sinica, Vol 51, Iss 2, Pp 269-278 (2022)

Subject Terms: building simplification, shape representation, adaptive simplification, graph convolutional autoencoder, Mathematical geography. Cartography, GA1-1776

Relation: http://xb.sinomaps.com/article/2022/1001-1595/2022-2-269.htm; https://doaj.org/toc/1001-1595; https://doaj.org/article/fb41dd4514ff464e84ec076c81d2f4de

Availability: https://doi.org/10.11947/j.AGCS.2022.20210302
https://doaj.org/article/fb41dd4514ff464e84ec076c81d2f4de

Table1_Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects.XLS

Authors: Ping Xuan Kai Xu Hui Cui et al.

Subject Terms: Pharmacology, Basic Pharmacology, Clinical Pharmacology and Therapeutics, Clinical Pharmacy and Pharmacy Practice, Pharmaceutical Sciences, Pharmacogenomics, Toxicology (incl. Clinical Toxicology), Pharmacology and Pharmaceutical Sciences not elsewhere classified, graph generative and adversarial strategy, topologies and attributes from heterogeneous graphs, graph convolutional autoencoder, self-calibrated pairwise attributes, representation-level attention

Availability: https://doi.org/10.3389/fphar.2023.1257842.s001
https://figshare.com/articles/dataset/Table1_Graph_generative_and_adversarial_strategy-enhanced_node_feature_learning_and_self-calibrated_pairwise_attribute_encoding_for_prediction_of_drug-related_side_effects_XLS/24079851

Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction.

Authors: Xuan P Wang M Liu Y et al.

Source: Briefings in bioinformatics [Brief Bioinform] 2022 May 13; Vol. 23 (3).

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE

MeSH Terms: Algorithms* , Neural Networks, Computer*, Drug Development/methods