Search Results - "variational graph convolutional autoencoder"

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

Exploring the Latent Information in Spatial Transcriptomics Data via Multi‐View Graph Convolutional Network Based on Implicit Contrastive Learning.

Authors: Ren, Sheng Liao, Xingyu Liu, Farong et al.

Source: Advanced Science; Jun2025, Vol. 12 Issue 21, p1-18, 18p

Subject Terms: GRAPH neural networks, IMPLICIT learning, GENE expression, TRANSCRIPTOMES, SPATIAL arrangement

INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation.

Authors: Liu, Yuyao Li, Zhen Chen, Xiaoyang et al.

Source: Nature Communications; 2/1/2025, Vol. 16 Issue 1, p1-23, 23p

Subject Terms: BIOLOGICAL variation, CHROMATIN, HERITABILITY, HETEROGENEITY, NOISE

Enhancing Gas Turbine Fault Diagnosis Using a Multi-Scale Dilated Graph Variational Autoencoder Model

Authors: Zhang Kun Li Hongren Wang Xin et al.

Source: IEEE Access, Vol 12, Pp 104818-104832 (2024)

Subject Terms: noise robustness, Gas turbine, 0202 electrical engineering, electronic engineering, information engineering, MG-VAE, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, fault diagnosis, 01 natural sciences, TK1-9971, 0104 chemical sciences

Access URL: https://doaj.org/article/c986c7c9e0e34b09bbf948eb168f8205

Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics

Authors: Zhen Li Xiaoyang Chen Xuegong Zhang et al.

Source: Genome Res

Subject Terms: Spatial Analysis, Gene Expression Profiling, Methods, Bayes Theorem, Neural Networks, Computer, Transcriptome

Access URL: https://pubmed.ncbi.nlm.nih.gov/37903634

Unsupervised spatially embedded deep representation of spatial transcriptomics.

Authors: Xu, Hang Fu, Huazhu Long, Yahui et al.

Source: Genome Medicine; 1/12/2024, Vol. 16 Issue 1, p1-15, 15p

Subject Terms: TRANSCRIPTOMES, SUPERVISED learning, GENE expression

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

Authors: Ma, Jian Guo, Jingjing Fan, Zhiwei et al.

Source: Biomolecules (2218-273X); May2023, Vol. 13 Issue 5, p767, 16p

Subject Terms: TRANSCRIPTOMES, GENE expression, DNA sequencing, GENETIC mutation, SPATIAL variation

SurfNet: Learning Surface Representations via Graph Convolutional Network.

Authors: Han, Jun Wang, Chaoli

Source: Computer Graphics Forum; Jun2022, Vol. 41 Issue 3, p109-120, 12p, 8 Color Photographs, 7 Diagrams, 5 Charts, 1 Graph

Subject Terms: REPRESENTATIONS of graphs, SCIENTIFIC visualization, DEEP learning, SPACE exploration

Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks.

Authors: KC, Kishan Li, Rui Cui, Feng et al.

Source: IEEE/ACM Transactions on Computational Biology & Bioinformatics; Mar/Apr2022, Vol. 19 Issue 2, p676-687, 12p