BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

4537 related articles for article (PubMed ID: 29409532)

  • 1. SCANPY: large-scale single-cell gene expression data analysis.
    Wolf FA; Angerer P; Theis FJ
    Genome Biol; 2018 Feb; 19(1):15. PubMed ID: 29409532
    [TBL] [Abstract][Full Text] [Related]  

  • 2. scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data.
    Faure L; Soldatov R; Kharchenko PV; Adameyko I
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36394263
    [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. Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data.
    Sturm G; Szabo T; Fotakis G; Haider M; Rieder D; Trajanoski Z; Finotello F
    Bioinformatics; 2020 Sep; 36(18):4817-4818. PubMed ID: 32614448
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A robust and accurate single-cell data trajectory inference method using ensemble pseudotime.
    Zhang Y; Tran D; Nguyen T; Dascalu SM; Harris FC
    BMC Bioinformatics; 2023 Feb; 24(1):55. PubMed ID: 36803767
    [TBL] [Abstract][Full Text] [Related]  

  • 6. switchde: inference of switch-like differential expression along single-cell trajectories.
    Campbell KR; Yau C
    Bioinformatics; 2017 Apr; 33(8):1241-1242. PubMed ID: 28011787
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge.
    Mukherjee S; Zhang Y; Fan J; Seelig G; Kannan S
    Bioinformatics; 2018 Jul; 34(13):i124-i132. PubMed ID: 29949988
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Single-cell gene set scoring with nearest neighbor graph smoothed data (gssnng).
    Gibbs DL; Strasser MK; Huang S
    Bioinform Adv; 2023; 3(1):vbad150. PubMed ID: 37886712
    [TBL] [Abstract][Full Text] [Related]  

  • 9. LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering.
    Specht AT; Li J
    Bioinformatics; 2017 Mar; 33(5):764-766. PubMed ID: 27993778
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.
    Qiu X; Rahimzamani A; Wang L; Ren B; Mao Q; Durham T; McFaline-Figueroa JL; Saunders L; Trapnell C; Kannan S
    Cell Syst; 2020 Mar; 10(3):265-274.e11. PubMed ID: 32135093
    [TBL] [Abstract][Full Text] [Related]  

  • 11. GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression.
    Tu W; Ling G; Liu F; Hu F; Song X
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(5):2945-2958. PubMed ID: 37037234
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A descriptive marker gene approach to single-cell pseudotime inference.
    Campbell KR; Yau C
    Bioinformatics; 2019 Jan; 35(1):28-35. PubMed ID: 29939207
    [TBL] [Abstract][Full Text] [Related]  

  • 13. SPRITE: improving spatial gene expression imputation with gene and cell networks.
    Sun ED; Ma R; Zou J
    Bioinformatics; 2024 Jun; 40(Supplement_1):i521-i528. PubMed ID: 38940132
    [TBL] [Abstract][Full Text] [Related]  

  • 14. High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.
    Skok Gibbs C; Jackson CA; Saldi GA; Tjärnberg A; Shah A; Watters A; De Veaux N; Tchourine K; Yi R; Hamamsy T; Castro DM; Carriero N; Gorissen BL; Gresham D; Miraldi ER; Bonneau R
    Bioinformatics; 2022 Apr; 38(9):2519-2528. PubMed ID: 35188184
    [TBL] [Abstract][Full Text] [Related]  

  • 15. PyLiger: scalable single-cell multi-omic data integration in Python.
    Lu L; Welch JD
    Bioinformatics; 2022 May; 38(10):2946-2948. PubMed ID: 35561174
    [TBL] [Abstract][Full Text] [Related]  

  • 16. scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data.
    Lyu H; Liu E; Wu Z; Li Y; Liu Y; Yin X
    Bioinformatics; 2022 Nov; 38(23):5151-5159. PubMed ID: 36205615
    [TBL] [Abstract][Full Text] [Related]  

  • 17. GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution.
    Strauss ME; Kirk PDW; Reid JE; Wernisch L
    Bioinformatics; 2020 Mar; 36(5):1484-1491. PubMed ID: 31608923
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Model-based branching point detection in single-cell data by K-branches clustering.
    Chlis NK; Wolf FA; Theis FJ
    Bioinformatics; 2017 Oct; 33(20):3211-3219. PubMed ID: 28582478
    [TBL] [Abstract][Full Text] [Related]  

  • 19. DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation.
    Wei J; Zhou T; Zhang X; Tian T
    Genomics Proteomics Bioinformatics; 2021 Apr; 19(2):306-318. PubMed ID: 33662626
    [TBL] [Abstract][Full Text] [Related]  

  • 20. ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor-ligand interactions that specify cell fate.
    Kyaw W; Chai RC; Khoo WH; Goldstein LD; Croucher PI; Murray JM; Phan TG
    Bioinformatics; 2023 Dec; 39(12):. PubMed ID: 38113422
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 227.