Suchergebnisse - Variation Graph Autoencoder (VGAE)

VGAE-Hub: a variational graph autoencoder framework for identifying shared hub genes in colorectal cancer and inflammatory bowel disease.

Autoren: Joshi, Pratibha Verma, Ravi Singh, Buddha

Quelle: Network Modeling & Analysis in Health Informatics & Bioinformatics; 11/4/2025, Vol. 14 Issue 1, p1-18, 18p

Importance weighted variational graph autoencoder.

Autoren: Tao, Yuhao Guo, Lin Zhao, Shuchang et al.

Quelle: Complex & Intelligent Systems; Jan2026, Vol. 12 Issue 1, p1-16, 16p

Automated Residential Bubble Diagram Generation Based on Dual-Branch Graph Neural Network and Variational Encoding.

Autoren: Luo, Gan Zhou, Xuhong Liao, Yunzhu et al.

Quelle: Applied Sciences (2076-3417); Apr2025, Vol. 15 Issue 8, p4490, 23p

Schlagwörter: GRAPH neural networks, AUTOENCODERS, FLOOR design & construction, IMPLICIT learning

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

Autoren: Jian Ma Jingjing Guo Zhiwei Fan et al.

Quelle: Biomolecules
Biomolecules, Vol 13, Iss 5, p 767 (2023)
Biomolecules; Volume 13; Issue 5; Pages: 767

Schlagwörter: copy number alteration, DNA Copy Number Variations, Gene Dosage, spatial transcriptome, Microbiology, QR1-502, Article, HMM, variational graph convolutional autoencoder, Neoplasms, Mutation, Humans, Transcriptome

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37238637
https://doaj.org/article/90463aa50daa432f92ab1ce3667e38d2

MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.

Autoren: Wang, Feng Chu, Jinming Shen, Liyan et al.

Quelle: BMC Biology; 8/11/2025, Vol. 23 Issue 1, p1-24, 24p

Schlagwörter: PROTEIN-protein interactions, DEEP learning, LANGUAGE models, GRAPH neural networks, BIOINFORMATICS, SACCHAROMYCES cerevisiae

Joint Spectral–Spatial Representation Learning for Unsupervised Hyperspectral Image Clustering.

Autoren: Liu, Xuanhao Wang, Taimao Wang, Xiaofeng

Quelle: Applied Sciences (2076-3417); Aug2025, Vol. 15 Issue 16, p8935, 14p

Schlagwörter: HYPERSPECTRAL imaging systems, CLUSTERING algorithms, ROBUST statistics, FEATURE extraction, GRAPH neural networks, PARTICLE swarm optimization, GENERALIZATION

DCMF-PPI: a protein-protein interaction predictor based on dynamic condition and multi-feature fusion.

Autoren: Chen, Siqi Zheng, Anhong Yu, Weichi et al.

Quelle: BMC Bioinformatics. 10/15/2025, Vol. 26 Issue 1, p1-27. 27p.

Schlagwörter: *PROTEIN-protein interactions, *DYNAMIC models, *CONVOLUTIONAL neural networks, *MOLECULAR dynamics, *GRAPH neural networks

Site selection and prediction of urban emergency shelter based on VGAE-RF model.

Autoren: Wang, Yong Han, Yaoyao Luo, An et al.

Quelle: Scientific Reports; 6/22/2024, Vol. 14 Issue 1, p1-18, 18p

Schlagwörter: RECEIVER operating characteristic curves, ARTIFICIAL intelligence, RANDOM forest algorithms, URBAN growth, EMERGENCY management

Geografische Kategorien: BEIJING (China)

Recognition of Yuan blue and white porcelain produced in Jingdezhen based on graph anomaly detection combining portable X-ray fluorescence spectrometry.

Autoren: Li, Jinwei Yang, Yifei Zou, Dongmian et al.

Quelle: Heritage Science; 3/6/2024, Vol. 12 Issue 1, p1-10, 10p

Schlagwörter: X-ray fluorescence, FLUORESCENCE spectroscopy, ELEMENTAL diet, POTTERY, THERMOLUMINESCENCE dating, GRAPH neural networks, PORCELAIN

Geografische Kategorien: JINGDEZHEN (China)

Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis.

Autoren: Yadalam, Pradeep K. Natarajan, Prabhu Manickam Ardila, Carlos M.

Quelle: Scientific Reports; 1/14/2025, Vol. 15 Issue 1, p1-11, 11p

Schlagwörter: APOPTOSIS, K-means clustering, AUTOENCODERS, PERIODONTAL disease, PORPHYROMONAS gingivalis

Signed versus isomorphic graph autoencoders for microRNA-single-nucleotide polymorphisms network reconstruction in periodontal osteo-genomic landscapes.

Alternate Title: Autocodificadores de grafos isomorfos versus firmados para la reconstrucción de redes de polimorfismos de un solo nucleótido de microARN en paisajes osteogenómicos periodontales. (Spanish)

Autoren: Sharma, Sarvagya Yadalam, Pradeep Kumar Ardila, Carlos M.

Quelle: Gaceta Médica de Caracas; jul-sep2025, Vol. 133 Issue 3, p675-685, 11p

Schlagwörter: MICRORNA, SINGLE nucleotide polymorphisms, RECONSTRUCTION (Graph theory), PERIODONTAL disease, MACHINE learning, GENOMICS, GRAPH neural networks, BIOLOGICAL models

MAVGAE: a multimodal framework for predicting asymmetric drug–drug interactions based on variational graph autoencoder.

Autoren: Deng, Zengqian Xu, Jie Feng, Yinfei et al.

Quelle: Computer Methods in Biomechanics & Biomedical Engineering; May2025, Vol. 28 Issue 7, p1098-1110, 13p

Schlagwörter: DRUG interactions, AUTOENCODERS, ARTIFICIAL intelligence, DRUG administration, POLYPHARMACY

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

Autoren: Mingye Wang Xiaohui Hu Pan Xie et al.

Quelle: Information Technology & Control; 2024, Vol. 53 Issue 2, p570-583, 14p

Schlagwörter: LANGUAGE models, LEARNING strategies, GRAPH neural networks, RECOMMENDER systems, BIPARTITE graphs

Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition.

Autoren: Abduljalil, Hosam Elhayek, Ahmed Marish Ali, Abdullah et al.

Quelle: AI; Sep2024, Vol. 5 Issue 3, p1695-1708, 14p

Schlagwörter: HUMAN activity recognition, DEEP learning, PATIENT monitoring, ALGORITHMS, SKELETON, AUTOENCODERS

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics.

Autoren: Lei, Lixin Han, Kaitai Wang, Zijun et al.

Quelle: Briefings in Bioinformatics; May2024, Vol. 25 Issue 3, p1-15, 15p

Schlagwörter: TRANSCRIPTOMES, GRAPH neural networks, PHENOMENOLOGICAL biology, GENE expression, HETEROGENEITY

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

Autoren: Liu, Lijun Wu, Xiaoyang Yu, Jun et al.

Quelle: Biology (2079-7737); Sep2024, Vol. 13 Issue 9, p713, 15p

Schlagwörter: ARTIFICIAL neural networks, RNA sequencing, SCALABILITY, HETEROGENEITY

Graph Autoencoder with Preserving Node Attribute Similarity.

Autoren: Lin, Mugang Wen, Kunhui Zhu, Xuanying et al.

Quelle: Entropy; Apr2023, Vol. 25 Issue 4, p567, 22p

Schlagwörter: REPRESENTATIONS of graphs, CITATION networks, SOIL structure, LEARNING ability, DECODING algorithms, CHANNEL coding

A Relationship Prediction Method for Magnaporthe oryzae –Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder.

Autoren: Zhao, Enshuang Dong, Liyan Zhao, Hengyi et al.

Quelle: Journal of Fungi; Oct2023, Vol. 9 Issue 10, p1007, 16p

Schlagwörter: MULTIOMICS, PYRICULARIA oryzae, RICE, GENE regulatory networks, DATA integration, NON-coding RNA, GENE expression

Hybrid graph-based models in medical decision support.

Autoren: Bouslimi, Riadh Zitouni, Fatma Ben Smida, Takwa et al.

Quelle: Network Modeling & Analysis in Health Informatics & Bioinformatics; 12/4/2025, Vol. 14 Issue 1, p1-21, 21p

Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep.

Autoren: Liu, Teng Fang, Zhaoyu Li, Xin et al.

Quelle: Bioinformatics; Jan2024, Vol. 40 Issue 1, p1-12, 12p

Schlagwörter: DEEP learning, TRANSCRIPTOMES, GENE expression profiling, LEARNING modules, SOURCE code