206 related articles for article (PubMed ID: 38819253)
1. Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.
Wang L; Hu Y; Xiao K; Zhang C; Shi Q; Chen L
Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38819253
[TBL] [Abstract][Full Text] [Related]
2. 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]
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. Complete spatially resolved gene expression is not necessary for identifying spatial domains.
Lin S; Cui Y; Zhao F; Yang Z; Song J; Yao J; Zhao Y; Qian BZ; Zhao Y; Yuan Z
Cell Genom; 2024 Jun; 4(6):100565. PubMed ID: 38781966
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. 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]
8. 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]
9. iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis.
Jiang X; Wang S; Guo L; Zhu B; Wen Z; Jia L; Xu L; Xiao G; Li Q
Genome Biol; 2024 Jun; 25(1):147. PubMed ID: 38844966
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. A modality-collaborative convolution and transformer hybrid network for unpaired multi-modal medical image segmentation with limited annotations.
Liu H; Zhuang Y; Song E; Xu X; Ma G; Cetinkaya C; Hung CC
Med Phys; 2023 Sep; 50(9):5460-5478. PubMed ID: 36864700
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.
Si Z; Li H; Shang W; Zhao Y; Kong L; Long C; Zuo Y; Feng Z
Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38811360
[TBL] [Abstract][Full Text] [Related]
14. Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning.
Zuo C; Zhang Y; Cao C; Feng J; Jiao M; Chen L
Nat Commun; 2022 Oct; 13(1):5962. PubMed ID: 36216831
[TBL] [Abstract][Full Text] [Related]
15. HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.
Ma Y; Liu L; Zhao Y; Hang B; Zhang Y
BMC Genomics; 2024 Jun; 25(1):566. PubMed ID: 38840049
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics.
Liang Y; Shi G; Cai R; Yuan Y; Xie Z; Yu L; Huang Y; Shi Q; Wang L; Li J; Tang Z
Nat Commun; 2024 Jan; 15(1):600. PubMed ID: 38238417
[TBL] [Abstract][Full Text] [Related]
18. Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure.
Liu J; Liu H; Gong S; Tang Z; Xie Y; Yin H; Niyoyita JP
Med Image Anal; 2021 Aug; 72():102135. PubMed ID: 34182202
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data.
Wang Y; Liu Z; Ma X
Genome Biol; 2024 May; 25(1):133. PubMed ID: 38783355
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
