These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
263 related articles for article (PubMed ID: 38243703)
1. Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep. Liu T; Fang Z; Li X; Zhang L; Cao DS; Li M; Yin M Bioinformatics; 2024 Jan; 40(1):. PubMed ID: 38243703 [TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
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. scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering. Wang Y; Yu Z; Li S; Bian C; Liang Y; Wong KC; Li X Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36734596 [TBL] [Abstract][Full Text] [Related]
7. 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]
8. DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax. Xiu YH; Sun SL; Zhou BW; Wan Y; Tang H; Long HX Methods; 2024 Nov; 231():226-236. PubMed ID: 39413889 [TBL] [Abstract][Full Text] [Related]
9. scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data. Zhai Y; Chen L; Deng M Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38678389 [TBL] [Abstract][Full Text] [Related]
10. Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network. Song T; Markham KK; Li Z; Muller KE; Greenham K; Kuang R Bioinformatics; 2022 Feb; 38(5):1344-1352. PubMed ID: 34864909 [TBL] [Abstract][Full Text] [Related]
11. Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics. Lei L; Han K; Wang Z; Shi C; Wang Z; Dai R; Zhang Z; Wang M; Guo Q Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38627939 [TBL] [Abstract][Full Text] [Related]
12. Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network. Gan Y; Huang X; Zou G; Zhou S; Guan J Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35172334 [TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. [Identifying spatial domains from spatial transcriptome by graph attention network]. Wu H; Gao J Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2024 Apr; 41(2):246-252. PubMed ID: 38686404 [TBL] [Abstract][Full Text] [Related]
16. A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics. Liu T; Fang ZY; Zhang Z; Yu Y; Li M; Yin MZ Comput Struct Biotechnol J; 2024 Dec; 23():106-128. PubMed ID: 38089467 [TBL] [Abstract][Full Text] [Related]
17. Unraveling Spatial Domain Characterization in Spatially Resolved Transcriptomics with Robust Graph Contrastive Clustering. Zhang Y; Yu Z; Wong KC; Li X Bioinformatics; 2024 Jul; 40(7):. PubMed ID: 39012523 [TBL] [Abstract][Full Text] [Related]
18. Parallelly Adaptive Graph Convolutional Clustering Model. He X; Wang B; Hu Y; Gao J; Sun Y; Yin B IEEE Trans Neural Netw Learn Syst; 2024 Apr; 35(4):4451-4464. PubMed ID: 35617184 [TBL] [Abstract][Full Text] [Related]
19. Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis. Chen C; Zhang Z; Tang P; Liu X; Huang B Comput Biol Med; 2024 May; 174():108449. PubMed ID: 38626512 [TBL] [Abstract][Full Text] [Related]
20. Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Duan Z; Riffle D; Li R; Liu J; Min MR; Zhang J Bioinformatics; 2024 Jun; 40(6):. PubMed ID: 38806165 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]