Suchergebnisse - Multi-modal graph autoencoder

Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder

Autoren: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Quelle: Reliability Engineering and System Safety. 267

Schlagwörter: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor

Zugangs-URL: https://research.chalmers.se/publication/549377

ZMGA: A ZINB-based multi-modal graph autoencoder enhancing topological consistency in single-cell clustering

Autoren: Jiaxi Yao Lin Li Tong Xu et al.

Quelle: Biomedical Signal Processing and Control. 97:106587

Spatially Aware Domain Adaptation Enables Cell Type Deconvolution from Multi‐Modal Spatially Resolved Transcriptomics.

Autoren: Wang, Lequn Bai, Xiaosheng Zhang, Chuanchao et al.

Quelle: Small Methods; May2025, Vol. 9 Issue 5, p1-14, 14p

Schlagwörter: REPRESENTATIONS of graphs, BIOLOGICAL systems, TRANSCRIPTOMES, AUTOENCODERS, RNA sequencing

ZMGA: A ZINB-based multi-modal graph autoencoder enhancing topological consistency in single-cell clustering.

Autoren: Yao, Jiaxi Li, Lin Xu, Tong et al.

Quelle: Biomedical Signal Processing & Control. Nov2024, Vol. 97, pN.PAG-N.PAG. 1p.

Schlagwörter: K-nearest neighbor classification, Cluster analysis (Statistics), Conformity

A Multi-modal Drug Target Affinity Prediction Based on Graph Features and Pre-trained Sequence Embeddings.

Autoren: Tang X Lei X Liu L

Quelle: Interdisciplinary sciences, computational life sciences [Interdiscip Sci] 2025 Dec; Vol. 17 (4), pp. 822-843. Date of Electronic Publication: 2025 Jun 02.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101515919 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-1462 (Electronic) Linking ISSN: 18671462 NLM ISO Abbreviation: Interdiscip Sci Subsets: MEDLINE

MeSH-Schlagworte: Computational Biology*/methods , Proteins*/chemistry , Proteins*/metabolism, Deep Learning ; Algorithms ; Humans ; Pharmaceutical Preparations/chemistry

A transformer guided multi modal learning framework for predictive and causal assessment of thermal runaway in high energy batteries

Autoren: Sameer Sheshrao Gajghate Muhamad Mat Noor Subhash Kumar et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-20 (2025)

Schlagwörter: Battery safety, Thermal runaway, Multi-Modal learning, Graph neural networks, Transformer models, Process, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/be8872b67555439dbba6a91979e67648

SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.

Autoren: Min, Wenwen Fang, Donghai Chen, Jinyu et al.

Quelle: PLoS Computational Biology; 4/3/2025, Vol. 21 Issue 4, p1-29, 29p

Schlagwörter: GRAPH neural networks, AUTOENCODERS, DEEP learning, TRANSCRIPTOMES, GENE expression

Combination of Graph and Convolutional Networks for Brain Tumor Segmentation from Multi-Modal MR Images In Clinical Applications.

Autoren: Vatanpour, Marjan Haddadnia, Javad Bajestani, Shahryar Salmani

Quelle: Frontiers in Biomedical Technologies; Summer2025, Vol. 12 Issue 3, p547-556, 10p

Schlagwörter: BRAIN tumors, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, GRAPH neural networks, IMAGE segmentation, DIAGNOSIS, MAGNETIC resonance imaging, DEEP learning

MaskPOI: A POI Representation Learning Method Using Graph Mask Modeling.

Autoren: Zhang, Haoyuan Shi, Zexi Li, Mei et al.

Quelle: Electronics (2079-9292); Apr2025, Vol. 14 Issue 7, p1242, 25p

Schlagwörter: GRAPH neural networks, SUPERVISED learning, AUTOENCODERS, LOCATION-based services, POPULATION density

NF-VGA: Incorporating Normalizing Flows into Graph Variational Autoencoder for Embedding Attribute Networks

Autoren: Pengfei Jiao Ziyang Liu Di Jin et al.

Quelle: 2020 IEEE International Conference on Data Mining (ICDM). :1244-1249

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/conf/icdm/icdm2020.html#ShanJJLLH20

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations

Autoren: Deng, Lei Huang, Yibiao Liu, Xuejun et al.

Index Begriffe: Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2108.06338

Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks

Autoren: Xiang Feng Fang Fang Haixia Long et al.

Quelle: Frontiers in Genetics, Vol 13 (2022)

Schlagwörter: single-cell RNA-seq, gene imputation, cell clustering, graph neural networks, graph autoencoders, graph attention networks, Genetics, QH426-470

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fgene.2022.1003711/full; https://doaj.org/toc/1664-8021

Zugangs-URL: https://doaj.org/article/602e360eeb464eb8b64af96af7059190

Attribute graph clustering via dual-driven network with dynamic structure optimization and prototype contrast.

Autoren: Tan, Shuqiu Li, Huibing

Quelle: Pattern Analysis & Applications; Dec2025, Vol. 28 Issue 4, p1-19, 19p

Multi-view representation model based on graph autoencoder.

Autoren: Li, Jingci Lu, Guangquan Wu, Zhengtian et al.

Quelle: Information Sciences. Jun2023, Vol. 632, p439-453. 15p.

Schlagwörter: *SOCIAL networks, REPRESENTATIONS of graphs, BIOLOGICAL networks, TOPOLOGY

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe–drug associations

Autoren: Lei Deng Yibiao Huang Xuejun Liu et al.

Quelle: Bioinformatics. 38:1118-1125

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Biological sciences, Humans, Computational Biology, Neural Networks, Computer, Quantitative Biology - Quantitative Methods, Algorithms, Software, Quantitative Methods (q-bio.QM), Machine Learning (cs.LG), 3. Good health

Zugangs-URL: http://arxiv.org/pdf/2108.06338
https://pubmed.ncbi.nlm.nih.gov/34864873
http://arxiv.org/abs/2108.06338

Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.

Autoren: Huang, Wendong Hu, Yaofeng Wang, Lequn et al.

Quelle: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-15. 15p.

Schlagwörter: *TRANSFORMER models, FUNCTIONAL regions, REPRESENTATIONS of graphs, AUTOENCODERS, TRANSCRIPTOMES

CGRC: Content-based Graph Reconstruction for Cold-start Item Recommendation ; 콘텐츠 기반 그래프 재구성을 통한 콜드 스타트 아이템

Autoren: 김진리 이준석 Jinri Kim et al.

Weitere Verfasser: 김진리 이준석 Jinri Kim et al.

Schlagwörter: Cold-start, Graph Neural Networks, Multi-modal, Masked Autoencoder

Time: 005

Dateibeschreibung: iii, 41

Relation: 000000181208; https://hdl.handle.net/10371/209643; https://dcollection.snu.ac.kr/common/orgView/000000181208; 000000000051▲000000000062▲000000181208▲

Verfügbarkeit: https://hdl.handle.net/10371/209643
https://dcollection.snu.ac.kr/common/orgView/000000181208

MMDDI-SSE: A Novel Multi-Modal Feature Fusion Model With Static Subgraph Embedding for Drug-Drug Interaction Event Prediction

Autoren: Guishen Wang Honghan Chen Handan Wang et al.

Quelle: IEEE Journal of Biomedical and Health Informatics. 29:6081-6091

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40067720

MKAN-MMI: empowering traditional medicine-microbe interaction prediction with masked graph autoencoders and KANs.

Autoren: Ye, Sheng Wang, Jue Zhu, Mingmin et al.

Quelle: Frontiers in Pharmacology; 2024, p1-14, 14p

Schlagwörter: GRAPH neural networks, ARTIFICIAL intelligence, BINOMIAL distribution, TRADITIONAL medicine, DATA extraction

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

Autoren: Zhu, Huan Hao, Hongxia Yu, Liang

Quelle: BMC Biology; 8/15/2024, Vol. 22 Issue 1, p1-15, 15p

Schlagwörter: MICROBIOTA, INTERPERSONAL relations, DATA distribution, INDIVIDUALIZED medicine, DISEASE progression, AUTOENCODERS