BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

197 related articles for article (PubMed ID: 38678389)

  • 1. 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]  

  • 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. scEVOLVE: cell-type incremental annotation without forgetting for single-cell RNA-seq data.
    Zhai Y; Chen L; Deng M
    Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38366803
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding.
    Gan Y; Yu J; Xu G; Yan C; Zou G
    Bioinformatics; 2024 May; 40(5):. PubMed ID: 38810116
    [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. DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics.
    Rahimi A; Vale-Silva LA; Fälth Savitski M; Tanevski J; Saez-Rodriguez J
    Nat Commun; 2024 Jun; 15(1):4994. PubMed ID: 38862466
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Simulating multiple variability in spatially resolved transcriptomics with scCube.
    Qian J; Bao H; Shao X; Fang Y; Liao J; Chen Z; Li C; Guo W; Hu Y; Li A; Yao Y; Fan X; Cheng Y
    Nat Commun; 2024 Jun; 15(1):5021. PubMed ID: 38866768
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics.
    Sang-Aram C; Browaeys R; Seurinck R; Saeys Y
    Elife; 2024 May; 12():. PubMed ID: 38787371
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.
    Laubscher E; Wang X; Razin N; Dougherty T; Xu RJ; Ombelets L; Pao E; Graf W; Moffitt JR; Yue Y; Van Valen D
    Cell Syst; 2024 May; 15(5):475-482.e6. PubMed ID: 38754367
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding.
    Li Y; Zhang J; Gao X; Zhang QC
    Cell Syst; 2024 Jun; 15(6):578-592.e7. PubMed ID: 38823396
    [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. 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]  

  • 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. HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data.
    Chen X; Lin J; Wang Y; Zhang W; Xie W; Zheng Z; Wong KC
    Bioinformatics; 2024 Jun; 40(6):. PubMed ID: 38837395
    [TBL] [Abstract][Full Text] [Related]  

  • 17. SIMBA: single-cell embedding along with features.
    Chen H; Ryu J; Vinyard ME; Lerer A; Pinello L
    Nat Methods; 2024 Jun; 21(6):1003-1013. PubMed ID: 37248389
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.
    Tiong KL; Luzhbin D; Yeang CH
    BMC Bioinformatics; 2024 Jun; 25(1):209. PubMed ID: 38867193
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.