BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

325 related articles for article (PubMed ID: 36859400)

  • 1. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST.
    Long Y; Ang KS; Li M; Chong KLK; Sethi R; Zhong C; Xu H; Ong Z; Sachaphibulkij K; Chen A; Zeng L; Fu H; Wu M; Lim LHK; Liu L; Chen J
    Nat Commun; 2023 Mar; 14(1):1155. PubMed ID: 36859400
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 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. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics.
    Swain AK; Pandit V; Sharma J; Yadav P
    Commun Biol; 2024 May; 7(1):639. PubMed ID: 38796505
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding.
    Yin W; Wan Y; Zhou Y
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38557675
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.
    Yao J; Yu J; Caffo B; Page SC; Martinowich K; Hicks SC
    bioRxiv; 2024 Feb; ():. PubMed ID: 38352580
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information.
    Li H; Li H; Zhou J; Gao X
    Bioinformatics; 2022 Oct; 38(21):4878-4884. PubMed ID: 36063455
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 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. DIST: spatial transcriptomics enhancement using deep learning.
    Zhao Y; Wang K; Hu G
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36653906
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level transcriptome to nuclear morphology.
    Zhu S; Kubota N; Wang S; Wang T; Xiao G; Hoshida Y
    bioRxiv; 2023 Dec; ():. PubMed ID: 38187541
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 17.