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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

198 related articles for article (PubMed ID: 39103939)

  • 1. STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks.
    Li Y; Luo Y
    Genome Biol; 2024 Aug; 25(1):206. PubMed ID: 39103939
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks.
    Li Y; Luo Y
    bioRxiv; 2023 Jun; ():. PubMed ID: 37333198
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Spatially informed cell-type deconvolution for spatial transcriptomics.
    Ma Y; Zhou X
    Nat Biotechnol; 2022 Sep; 40(9):1349-1359. PubMed ID: 35501392
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve.
    Charytonowicz D; Brody R; Sebra R
    Nat Commun; 2023 Mar; 14(1):1350. PubMed ID: 36906603
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network.
    Yang W; Wang P; Xu S; Wang T; Luo M; Cai Y; Xu C; Xue G; Que J; Ding Q; Jin X; Yang Y; Pang F; Pang B; Lin Y; Nie H; Xu Z; Ji Y; Jiang Q
    Nat Commun; 2024 Aug; 15(1):7101. PubMed ID: 39155292
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.
    Srinivasan S; Leshchyk A; Johnson NT; Korkin D
    RNA; 2020 Oct; 26(10):1303-1319. PubMed ID: 32532794
    [TBL] [Abstract][Full Text] [Related]  

  • 9. SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology.
    Ding J; Li L; Lu Q; Venegas J; Wang Y; Wu L; Jin W; Wen H; Liu R; Tang W; Dai X; Li Z; Zuo W; Chang Y; Lei YL; Shang L; Danaher P; Xie Y; Tang J
    J Comput Biol; 2024 Sep; 31(9):871-885. PubMed ID: 39117342
    [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. Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry.
    Zhang Q; Jiang S; Schroeder A; Hu J; Li K; Zhang B; Dai D; Lee EB; Xiao R; Li M
    Nat Commun; 2023 Jul; 14(1):4050. PubMed ID: 37422469
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks.
    Baul S; Tanvir Ahmed K; Jiang Q; Wang G; Li Q; Yong J; Zhang W
    Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38960406
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.
    Chen J; Liu W; Luo T; Yu Z; Jiang M; Wen J; Gupta GP; Giusti P; Zhu H; Yang Y; Li Y
    Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35753702
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD).
    Wang C; Acosta D; McNutt M; Bian J; Ma A; Fu H; Ma Q
    Nat Commun; 2024 Jun; 15(1):4710. PubMed ID: 38844475
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Computational solutions for spatial transcriptomics.
    Kleino I; Frolovaitė P; Suomi T; Elo LL
    Comput Struct Biotechnol J; 2022; 20():4870-4884. PubMed ID: 36147664
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparative Methods for Demystifying Spatial Transcriptomics.
    Sammeth M; Mudra S; Bialdiga S; Hartmannsberger B; Kramer S; Rittner H
    Methods Mol Biol; 2024; 2802():515-546. PubMed ID: 38819570
    [TBL] [Abstract][Full Text] [Related]  

  • 17. MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data.
    Fan J; Lyu Y; Zhang Q; Wang X; Li M; Xiao R
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36208175
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Tissue RNA Integrity in Visium Spatial Protocol (Fresh Frozen Samples).
    Antico F; Gai M; Arigoni M
    Methods Mol Biol; 2023; 2584():191-203. PubMed ID: 36495450
    [TBL] [Abstract][Full Text] [Related]  

  • 19. scPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement.
    Guo Q; Yuan M; Zhang L; Deng M
    Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38935069
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Multi-View Clustering With Graph Learning for scRNA-Seq Data.
    Wu W; Zhang W; Hou W; Ma X
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3535-3546. PubMed ID: 37486829
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.