366 related articles for article (PubMed ID: 35365632)
1. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder.
Dong K; Zhang S
Nat Commun; 2022 Apr; 13(1):1739. PubMed ID: 35365632
[TBL] [Abstract][Full Text] [Related]
2. SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics.
Li J; Wang J; Lin Z
Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38171928
[TBL] [Abstract][Full Text] [Related]
3. Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks.
Shi X; Zhu J; Long Y; Liang C
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37544658
[TBL] [Abstract][Full Text] [Related]
4. STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering.
Peng L; He X; Peng X; Li Z; Zhang L
Comput Biol Med; 2023 Nov; 166():107440. PubMed ID: 37738898
[TBL] [Abstract][Full Text] [Related]
5. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network.
Huo Y; Guo Y; Wang J; Xue H; Feng Y; Chen W; Li X
J Genet Genomics; 2023 Sep; 50(9):720-733. PubMed ID: 37356752
[TBL] [Abstract][Full Text] [Related]
6. stAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics.
Fang Z; Liu T; Zheng R; A J; Yin M; Li M
Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38189544
[TBL] [Abstract][Full Text] [Related]
7. High-density generation of spatial transcriptomics with STAGE.
Li S; Gai K; Dong K; Zhang Y; Zhang S
Nucleic Acids Res; 2024 May; 52(9):4843-4856. PubMed ID: 38647109
[TBL] [Abstract][Full Text] [Related]
8. BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices.
Tao Y; Sun X; Wang F
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38385877
[TBL] [Abstract][Full Text] [Related]
9. Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder.
Cao L; Yang C; Hu L; Jiang W; Ren Y; Xia T; Xu M; Ji Y; Li M; Xu X; Li Y; Zhang Y; Fang S
Gigascience; 2024 Jan; 13(1):. PubMed ID: 38373745
[TBL] [Abstract][Full Text] [Related]
10. A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data.
Zhang L; Liang S; Wan L
Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38801701
[TBL] [Abstract][Full Text] [Related]
11. Unsupervised spatially embedded deep representation of spatial transcriptomics.
Xu H; Fu H; Long Y; Ang KS; Sethi R; Chong K; Li M; Uddamvathanak R; Lee HK; Ling J; Chen A; Shao L; Liu L; Chen J
Genome Med; 2024 Jan; 16(1):12. PubMed ID: 38217035
[TBL] [Abstract][Full Text] [Related]
12. Graph deep learning enabled spatial domains identification for spatial transcriptomics.
Liu T; Fang ZY; Li X; Zhang LN; Cao DS; Yin MZ
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37080761
[TBL] [Abstract][Full Text] [Related]
13. Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.
Zeng Y; Yin R; Luo M; Chen J; Pan Z; Lu Y; Yu W; Yang Y
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36781228
[TBL] [Abstract][Full Text] [Related]
14. Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network.
Li X; Huang W; Xu X; Zhang HY; Shi Q
Front Genet; 2023; 14():1202409. PubMed ID: 37303949
[TBL] [Abstract][Full Text] [Related]
15. Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics.
Hu Y; Xiao K; Yang H; Liu X; Zhang C; Shi Q
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38324623
[TBL] [Abstract][Full Text] [Related]
16. Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism.
Wang B; Luo J; Liu Y; Shi W; Xiong Z; Shen C; Long Y
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37466210
[TBL] [Abstract][Full Text] [Related]
17. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network.
Hu J; Li X; Coleman K; Schroeder A; Ma N; Irwin DJ; Lee EB; Shinohara RT; Li M
Nat Methods; 2021 Nov; 18(11):1342-1351. PubMed ID: 34711970
[TBL] [Abstract][Full Text] [Related]
18. A contrastive learning approach to integrate spatial transcriptomics and histological images.
Lin Y; Liang Y; Wang D; Chang Y; Ma Q; Wang Y; He F; Xu D
Comput Struct Biotechnol J; 2024 Dec; 23():1786-1795. PubMed ID: 38707535
[TBL] [Abstract][Full Text] [Related]
19. Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST.
Liu W; Liao X; Luo Z; Yang Y; Lau MC; Jiao Y; Shi X; Zhai W; Ji H; Yeong J; Liu J
Nat Commun; 2023 Jan; 14(1):296. PubMed ID: 36653349
[TBL] [Abstract][Full Text] [Related]
20. Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks.
Li Y; Lu Y; Kang C; Li P; Chen L
Research (Wash D C); 2023; 6():0228. PubMed ID: 37736108
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
