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 *

276 related articles for article (PubMed ID: 27215581)

  • 1. SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.
    Welch JD; Hartemink AJ; Prins JF
    Genome Biol; 2016 May; 17(1):106. PubMed ID: 27215581
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Approximate inference of gene regulatory network models from RNA-Seq time series data.
    Thorne T
    BMC Bioinformatics; 2018 Apr; 19(1):127. PubMed ID: 29642837
    [TBL] [Abstract][Full Text] [Related]  

  • 3. SCnorm: robust normalization of single-cell RNA-seq data.
    Bacher R; Chu LF; Leng N; Gasch AP; Thomson JA; Stewart RM; Newton M; Kendziorski C
    Nat Methods; 2017 Jun; 14(6):584-586. PubMed ID: 28418000
    [TBL] [Abstract][Full Text] [Related]  

  • 4. scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data.
    Zheng X; Huang Y; Zou X
    PLoS Comput Biol; 2020 Jul; 16(7):e1007471. PubMed ID: 32716923
    [TBL] [Abstract][Full Text] [Related]  

  • 5. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation.
    Matsumoto H; Kiryu H; Furusawa C; Ko MSH; Ko SBH; Gouda N; Hayashi T; Nikaido I
    Bioinformatics; 2017 Aug; 33(15):2314-2321. PubMed ID: 28379368
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data.
    Lamere AT; Li J
    Methods Mol Biol; 2019; 1935():141-153. PubMed ID: 30758825
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Inference of differentiation time for single cell transcriptomes using cell population reference data.
    Sun N; Yu X; Li F; Liu D; Suo S; Chen W; Chen S; Song L; Green CD; McDermott J; Shen Q; Jing N; Han JJ
    Nat Commun; 2017 Nov; 8(1):1856. PubMed ID: 29187729
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Network embedding-based representation learning for single cell RNA-seq data.
    Li X; Chen W; Chen Y; Zhang X; Gu J; Zhang MQ
    Nucleic Acids Res; 2017 Nov; 45(19):e166. PubMed ID: 28977434
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.
    Korthauer KD; Chu LF; Newton MA; Li Y; Thomson J; Stewart R; Kendziorski C
    Genome Biol; 2016 Oct; 17(1):222. PubMed ID: 27782827
    [TBL] [Abstract][Full Text] [Related]  

  • 10. BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process.
    Boukouvalas A; Hensman J; Rattray M
    Genome Biol; 2018 May; 19(1):65. PubMed ID: 29843817
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Comparative Analysis of Single-Cell RNA Sequencing Methods.
    Ziegenhain C; Vieth B; Parekh S; Reinius B; Guillaumet-Adkins A; Smets M; Leonhardt H; Heyn H; Hellmann I; Enard W
    Mol Cell; 2017 Feb; 65(4):631-643.e4. PubMed ID: 28212749
    [TBL] [Abstract][Full Text] [Related]  

  • 12. TASIC: determining branching models from time series single cell data.
    Rashid S; Kotton DN; Bar-Joseph Z
    Bioinformatics; 2017 Aug; 33(16):2504-2512. PubMed ID: 28379537
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Continuous-state HMMs for modeling time-series single-cell RNA-Seq data.
    Lin C; Bar-Joseph Z
    Bioinformatics; 2019 Nov; 35(22):4707-4715. PubMed ID: 31038684
    [TBL] [Abstract][Full Text] [Related]  

  • 14. BEARscc determines robustness of single-cell clusters using simulated technical replicates.
    Severson DT; Owen RP; White MJ; Lu X; Schuster-Böckler B
    Nat Commun; 2018 Mar; 9(1):1187. PubMed ID: 29567991
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Detection of high variability in gene expression from single-cell RNA-seq profiling.
    Chen HI; Jin Y; Huang Y; Chen Y
    BMC Genomics; 2016 Aug; 17 Suppl 7(Suppl 7):508. PubMed ID: 27556924
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.
    Peng J; Wang X; Shang X
    BMC Bioinformatics; 2019 Jun; 20(Suppl 8):284. PubMed ID: 31182005
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification.
    Deng Y; Bao F; Yang Y; Ji X; Du M; Zhang Z; Wang M; Dai Q
    Nucleic Acids Res; 2017 Sep; 45(15):e143. PubMed ID: 28911101
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Diffusion maps for high-dimensional single-cell analysis of differentiation data.
    Haghverdi L; Buettner F; Theis FJ
    Bioinformatics; 2015 Sep; 31(18):2989-98. PubMed ID: 26002886
    [TBL] [Abstract][Full Text] [Related]  

  • 19. QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization.
    Zhao S; Xi L; Quan J; Xi H; Zhang Y; von Schack D; Vincent M; Zhang B
    BMC Genomics; 2016 Jan; 17():39. PubMed ID: 26747388
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Normalization and noise reduction for single cell RNA-seq experiments.
    Ding B; Zheng L; Zhu Y; Li N; Jia H; Ai R; Wildberg A; Wang W
    Bioinformatics; 2015 Jul; 31(13):2225-7. PubMed ID: 25717193
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.